{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Interactive Variogram Calculation Demonstration\n",
    "\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "\n",
    "### The Interactive Workflow\n",
    "\n",
    "Here's an interactive workflow for calculating directional experimental variograms in 2D. \n",
    "\n",
    "* setting the variogram calculation parameters for identifying spatial data pairs \n",
    "\n",
    "This approach is essential for quantifying spatial continuity with sparsely sampled, irregular spatial data.\n",
    "\n",
    "I have more comprehensive workflows for variogram calculation:\n",
    "\n",
    "* [Experimental Variogram Calculation in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_variogram_calculation.ipynb)\n",
    "\n",
    "* [Determination of Major and Minor Spatial Continuity Directions in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_spatial_continuity_directions.ipynb)\n",
    "\n",
    "#### Spatial Continuity \n",
    "\n",
    "**Spatial Continuity** is the correlation between values over distance.\n",
    "\n",
    "* No spatial continuity – no correlation between values over distance, random values at each location in space regardless of separation distance.\n",
    "\n",
    "* Homogenous phenomenon have perfect spatial continuity, since all values as the same (or very similar) they are correlated. \n",
    "\n",
    "We need a statistic to quantify spatial continuity! A convenient method is the Semivariogram.\n",
    "\n",
    "#### The Semivariogram\n",
    "\n",
    "Function of difference over distance.\n",
    "\n",
    "* The expected (average) squared difference between values separated by a lag distance vector (distance and direction), $h$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\gamma(\\bf{h}) = \\frac{1}{2 N(\\bf{h})} \\sum^{N(\\bf{h})}_{\\alpha=1} (z(\\bf{u}_\\alpha) - z(\\bf{u}_\\alpha + \\bf{h}))^2  \n",
    "\\end{equation}\n",
    "\n",
    "where $z(\\bf{u}_\\alpha)$ and $z(\\bf{u}_\\alpha + \\bf{h})$ are the spatial sample values at tail and head locations of the lag vector respectively.\n",
    "\n",
    "* Calculated over a suite of lag distances to obtain a continuous function.\n",
    "\n",
    "* the $\\frac{1}{2}$ term converts a variogram into a semivariogram, but in practice the term variogram is used instead of semivariogram.\n",
    "* We prefer the semivariogram because it relates directly to the covariance function, $C_x(\\bf{h})$ and univariate variance, $\\sigma^2_x$:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "Note the correlogram is related to the covariance function as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\frac{C_x(\\bf{h})}{\\sigma^2_x}\n",
    "\\end{equation}\n",
    "\n",
    "The correlogram provides of function of the $\\bf{h}-\\bf{h}$ scatter plot correlation vs. lag offset $\\bf{h}$.  \n",
    "\n",
    "\\begin{equation}\n",
    "-1.0 \\le \\rho_x(\\bf{h}) \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "#### Variogram Observations\n",
    "\n",
    "The following are common observations for variograms that should assist with their practical use.\n",
    "\n",
    "##### Observation \\#1 - As distance increases, variability increase (in general).\n",
    "\n",
    "This is common since in general, over greater distance offsets, there is often more difference between the head and tail samples.\n",
    "\n",
    "In some cases, such as with spatial cyclicity of the hole effect variogram model the variogram may have negative slope over somelag distance intervals\n",
    "\n",
    "Negative slopes at lag distances greater than half the data extent are often caused by too few pairs for a reliable variogram calculation\n",
    "\n",
    "##### Observation \\#2 - Calculated with over all possible pairs separated by lag vector, $\\bf{𝐡}$.\n",
    "\n",
    "We scan through the entire data set, searching for all possible pair combinations with all other data.  We then calculate the variogram as one half the expectation of squared difference between all pairs.\n",
    "\n",
    "More pairs results in a more reliable measure.\n",
    "\n",
    "##### Observation \\#3 - Need to plot the sill to know the degree of correlation.\n",
    "\n",
    "**Sill** is the variance, $\\sigma^2_x$\n",
    "\n",
    "Given stationarity of the variance, $\\sigma^2_x$, and variogram $\\gamma(\\bf{h})$:\n",
    "\n",
    "we can define the covariance function:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "The covariance measure is a measure of similarity over distance (the mirror image of the variogram as shown by the equation above).\n",
    "\n",
    "Given a standardized distribution $\\sigma^2_x = 1.0$, the covariance, $C_x(\\bf{h})$, is equal to the correlogram, $\\rho_x(\\bf{h})$: \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "##### Observation \\#4 - The lag distance at which the variogram reaches the sill is know as the range.\n",
    "\n",
    "At the range, knowing the data value at the tail location provides no information about a value at the head location of the lag distance vector.\n",
    "\n",
    "##### Observation \\#5 - The nugget effect, a discontinuity at the origin\n",
    "\n",
    "Sometimes there is a discontinuity in the variogram at distances less than the minimum data spacing.  This is known as **nugget effect**.\n",
    "\n",
    "The ratio of nugget / sill, is known as relative nugget effect (%). Modeled as a discontinuity with no correlation structure that at lags, $h \\gt \\epsilon$, an infinitesimal lag distance, and perfect correlation at $\\bf{h} = 0$.\n",
    "Caution when including nuggect effect in the variogram model as measurement error, mixing populations cause apparent nugget effect\n",
    "\n",
    "This exercise demonstrates the semivariogram calculation with GeostatsPy. The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. sample from the simulation\n",
    "3. calculate and visualize experimental semivariograms\n",
    "\n",
    "#### Variogram Calculation Parameters\n",
    "\n",
    "The variogram calculation parameters include:\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth (isotropic variograms are calculated with an azimuth tolerance of to 90.0)\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance, usually set to the minimum data spacing\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance, commonly set to 50% of unit lag distanceonal smoothing\n",
    "\n",
    "* **number of lags** - set based on the spatial extent of the dataset, we can typically calculate reliable variograms up to 1/2 the extent of the dataset\n",
    "\n",
    "* **bandwidth** is the maximum offset allowable from the lag vector \n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data.csv at https://git.io/fh4gm.\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB                       # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                 # GSLIB methods convert to Python    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os                                               # to set current working directory \n",
    "import sys                                              # supress output to screen for interactive variogram modeling\n",
    "import io\n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "import matplotlib.pyplot as plt                         # plotting\n",
    "from matplotlib.pyplot import cm                        # color maps\n",
    "from ipywidgets import interactive                      # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "from ipywidgets import VBox, HBox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"d:/PGE383\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>259</td>\n",
       "      <td>190</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0.041122</td>\n",
       "      <td>0.120862</td>\n",
       "      <td>6367.442357</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>501</td>\n",
       "      <td>270</td>\n",
       "      <td>49</td>\n",
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       "      <td>0.043853</td>\n",
       "      <td>0.094627</td>\n",
       "      <td>6906.176965</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>273</td>\n",
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       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>0.062169</td>\n",
       "      <td>0.408779</td>\n",
       "      <td>6244.965113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>159</td>\n",
       "      <td>380</td>\n",
       "      <td>259</td>\n",
       "      <td>0</td>\n",
       "      <td>0.064295</td>\n",
       "      <td>0.322764</td>\n",
       "      <td>7113.679894</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>96</td>\n",
       "      <td>870</td>\n",
       "      <td>449</td>\n",
       "      <td>0</td>\n",
       "      <td>0.065537</td>\n",
       "      <td>0.672005</td>\n",
       "      <td>5672.957304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index    X    Y  Facies  Porosity      Perm           AI\n",
       "0    259  190   19       0  0.041122  0.120862  6367.442357\n",
       "1    501  270   49       0  0.043853  0.094627  6906.176965\n",
       "2    273  270  219       0  0.062169  0.408779  6244.965113\n",
       "3    159  380  259       0  0.064295  0.322764  7113.679894\n",
       "4     96  870  449       0  0.065537  0.672005  5672.957304"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"sample_data_MV_biased.csv\")           # read a .csv file in as a DataFrame\n",
    "#print(df.iloc[0:5,:])                                  # display first 4 samples in the table as a preview\n",
    "df.head()                                               # we could also use this command for a table preview "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will work with all facies pooled together. I wanted to simplify this workflow and focus more on spatial continuity direction detection. Finally, by not using facies we do have more samples to support our statistical inference. Most often facies are essential in the subsurface model. Don't worry we will check if this is reasonable in a bit.   \n",
    "\n",
    "You are welcome to repeat this workflow on a by-facies basis.  The following code could be used to build DataFrames ('df_sand' and 'df_shale') for each facies.\n",
    "\n",
    "```p\n",
    "df_sand = pd.DataFrame.copy(df[df['Facies'] == 1]).reset_index()  # copy only 'Facies' = sand records\n",
    "df_shale = pd.DataFrame.copy(df[df['Facies'] == 0]).reset_index() # copy only 'Facies' = shale records\n",
    "```\n",
    "\n",
    "Let's look at summary statistics for all facies combined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Index</th>\n",
       "      <td>367.0</td>\n",
       "      <td>292.926431</td>\n",
       "      <td>169.167110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>367.0</td>\n",
       "      <td>500.108992</td>\n",
       "      <td>289.978309</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>765.000000</td>\n",
       "      <td>990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>367.0</td>\n",
       "      <td>520.403270</td>\n",
       "      <td>277.752407</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>539.000000</td>\n",
       "      <td>769.000000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>367.0</td>\n",
       "      <td>0.596730</td>\n",
       "      <td>0.491224</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>367.0</td>\n",
       "      <td>0.126874</td>\n",
       "      <td>0.030546</td>\n",
       "      <td>0.041122</td>\n",
       "      <td>0.103398</td>\n",
       "      <td>0.125616</td>\n",
       "      <td>0.148323</td>\n",
       "      <td>0.210258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>367.0</td>\n",
       "      <td>83.313352</td>\n",
       "      <td>224.350475</td>\n",
       "      <td>0.094627</td>\n",
       "      <td>2.288119</td>\n",
       "      <td>10.294907</td>\n",
       "      <td>50.219914</td>\n",
       "      <td>1991.097723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>367.0</td>\n",
       "      <td>4790.458884</td>\n",
       "      <td>975.582304</td>\n",
       "      <td>1981.177309</td>\n",
       "      <td>4108.843701</td>\n",
       "      <td>4712.673154</td>\n",
       "      <td>5466.957467</td>\n",
       "      <td>7561.250336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count         mean         std          min          25%  \\\n",
       "Index     367.0   292.926431  169.167110     0.000000   150.000000   \n",
       "X         367.0   500.108992  289.978309     0.000000   240.000000   \n",
       "Y         367.0   520.403270  277.752407     9.000000   269.000000   \n",
       "Facies    367.0     0.596730    0.491224     0.000000     0.000000   \n",
       "Porosity  367.0     0.126874    0.030546     0.041122     0.103398   \n",
       "Perm      367.0    83.313352  224.350475     0.094627     2.288119   \n",
       "AI        367.0  4790.458884  975.582304  1981.177309  4108.843701   \n",
       "\n",
       "                  50%          75%          max  \n",
       "Index      295.000000   440.000000   586.000000  \n",
       "X          500.000000   765.000000   990.000000  \n",
       "Y          539.000000   769.000000   999.000000  \n",
       "Facies       1.000000     1.000000     1.000000  \n",
       "Porosity     0.125616     0.148323     0.210258  \n",
       "Perm        10.294907    50.219914  1991.097723  \n",
       "AI        4712.673154  5466.957467  7561.250336  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()                               # summary table of sand only DataFrame statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's transform the porosity and permeaiblity data to standard normal (mean = 0.0, standard deviation = 1.0, Gaussian shape). This is required for sequential Gaussian simulation (common target for our variogram models) and the Gaussian transform assists with outliers and provides more interpretable variograms. \n",
    "\n",
    "Let's look at the inputs for the GeostatsPy nscore program.  Note the output include an ndarray with the transformed values (in the same order as the input data in Dataframe 'df' and column 'vcol'), and the transformation table in original values and also in normal score values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.nscore(df, vcol, wcol=None, ismooth=False, dfsmooth=None, smcol=0, smwcol=0)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.nscore                                         # see the input parameters required by the nscore function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following command will transform the Porosity and Permeabilty to standard normal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform to Gaussian by Facies\n",
    "df['NPor'], tvPor, tnsPor = geostats.nscore(df, 'Porosity') # nscore transform for all facies porosity \n",
    "df['NPerm'], tvPermSand, tnsPermSand = geostats.nscore(df, 'Perm')  # nscore transform for all facies permeability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the updated DataFrame to make sure that we now have the normal score porosity and permeability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>NPor</th>\n",
       "      <th>NPerm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>259</td>\n",
       "      <td>190</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0.041122</td>\n",
       "      <td>0.120862</td>\n",
       "      <td>6367.442357</td>\n",
       "      <td>-2.671293</td>\n",
       "      <td>-2.644781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>501</td>\n",
       "      <td>270</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043853</td>\n",
       "      <td>0.094627</td>\n",
       "      <td>6906.176965</td>\n",
       "      <td>-2.644781</td>\n",
       "      <td>-2.775560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>273</td>\n",
       "      <td>270</td>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>0.062169</td>\n",
       "      <td>0.408779</td>\n",
       "      <td>6244.965113</td>\n",
       "      <td>-2.467028</td>\n",
       "      <td>-1.789282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>159</td>\n",
       "      <td>380</td>\n",
       "      <td>259</td>\n",
       "      <td>0</td>\n",
       "      <td>0.064295</td>\n",
       "      <td>0.322764</td>\n",
       "      <td>7113.679894</td>\n",
       "      <td>-2.344090</td>\n",
       "      <td>-1.945032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>96</td>\n",
       "      <td>870</td>\n",
       "      <td>449</td>\n",
       "      <td>0</td>\n",
       "      <td>0.065537</td>\n",
       "      <td>0.672005</td>\n",
       "      <td>5672.957304</td>\n",
       "      <td>-2.248832</td>\n",
       "      <td>-1.316486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index    X    Y  Facies  Porosity      Perm           AI      NPor  \\\n",
       "0    259  190   19       0  0.041122  0.120862  6367.442357 -2.671293   \n",
       "1    501  270   49       0  0.043853  0.094627  6906.176965 -2.644781   \n",
       "2    273  270  219       0  0.062169  0.408779  6244.965113 -2.467028   \n",
       "3    159  380  259       0  0.064295  0.322764  7113.679894 -2.344090   \n",
       "4     96  870  449       0  0.065537  0.672005  5672.957304 -2.248832   \n",
       "\n",
       "      NPerm  \n",
       "0 -2.644781  \n",
       "1 -2.775560  \n",
       "2 -1.789282  \n",
       "3 -1.945032  \n",
       "4 -1.316486  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()                                               # preview sand DataFrame with nscore transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks good! One way to check is to see if the relative magnitudes of the normal score transformed values match the original values.  e.g. that the normal score transform of 0.10 porosity normal score is less than the normal score transform of 0.14 porsity.  Also, the normal score transform of values close to the mean value should be close to 0.0 \n",
    "\n",
    "Let's also check the original and transformed sand and shale porosity distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)                                        # plot original sand and shale porosity histograms\n",
    "plt.hist(df['Porosity'], facecolor='red',bins=np.linspace(0.0,0.25,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label='Original')\n",
    "plt.xlim([0.05,0.25]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Porosity')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(222)  \n",
    "plt.hist(df['NPor'], facecolor='blue',bins=np.linspace(-3.0,3.0,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label = 'Trans')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Nscore Porosity')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(223)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df['Perm'], facecolor='red',bins=np.linspace(0.0,1000.0,100000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label='Original')\n",
    "plt.xlim([0.0,1000.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Permeability')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(224)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df['NPerm'], facecolor='blue',bins=np.linspace(-3.0,3.0,100000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label = 'Trans')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Permeability (mD)'); plt.ylabel('Frequency'); plt.title('Nscore Permeability')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normal score transform has correctly transformed the porosity and permeability to standard normal.\n",
    "\n",
    "#### Inspection of Posted Data\n",
    "\n",
    "Data visualization is very useful to detect patterns. Our brains are very good at pattern detection. I promote quantitative methods and recognize issues with cognitive bias, but it is important to recognize the value is expert intepretation based on data visualization.\n",
    "\n",
    "* This data visualization will also be important to assist with parameter selection for the quantitative methods later.\n",
    "\n",
    "Let's plot the location maps of normal score transforms of porosity and permeability for all facies. We will also include a cross plot of the nscore permeability vs. porosity colored by facies to aid with comparison in spatial features between the porosity and permeability data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eKM/rGZR7+i3KYkSg5PtBIUQyyiIwwwsYOaOh8aDQ9LUQ/kP6+hXKXE/TlbctgXncrde5o9QfEUK8JoQ4XUw7s3kVZR5rOLAJZW2R7YWEL6zuXxnYgTLfdC+wVOazdyqKzgJECyGOmLh/idJhWWZfP+HuqkgaGqWCEOJ3YIOUcsXDtkVDQ0Pj34b65W8dEKB+QdHQ0NDQKAU0fb03QlmHJQJl1fQLZXWe/8RmphqPBupQkAZAaQ7l0NDQ0CgXqEPxRgErtMqRhoaGRumh6WuRGQocLMvGJ5ThEFwhxCqhbMp7ysTNRQixXQhxQf3rbOL3tlA2Xz0nhGhn4t5QKCvfXRRCfKSOadZ4xBBCrEX55D9a5lzJS+MRR51zcEAoGxGfFkJMzyeMUMvfRaFsPN3AxK+9Wm4vCpPV1gor7+UNTQ817oVQNqHPXkX7w4dsTrlF08N/F0XJLw0NTV+LhhDiCkojfVxZn6ss54CuAdrncpsI7JRSVkbZnHoigBCiBso8xJpqnKVCCDM1zqfAIJQxzZXzSVPjEUBK2UdK6SilXPOwbdEoNulAKyllXZT5Je2FsiiTKc9ytwwOQimXqOV0iepfA3hVLc9QQHkvp6xB00ONQpBSnpVS2kopm0kpEx62PeUYTQ//XRQlvzTKOZq+Fg0pZYBUtog5WtbnKrMGqJTyL5QJ16Z0RpkAjPq3i4n7Rillurr4y0WgiRDCE3CQUu6VymTVL0ziaGholAJSIUn9V68euSeHdwa+UMPuA5zU8tkEuCilvKwunrCRu0OwCyrv5Q5NDzU0/h1oevjvooj5paGh8YjxoOeAekgpb4Kyt5cQwl119ybnEsvXVbdM9Xdu93wRQgxC6Y3E1ta2YbVq1UrR9KKRmZlJ+OVruFnlXFU605BFonkWPn6+BcTUeJQwGAyEXbpMReuc+ZhlNBKZlYq5UeJuZZfDL82QSbK5wMunwEe0UA4fPhwlpXS7V7i27dxldFS+C+Xly9Ej8adRVprLZrmUcrlpGLXn/jDKPn5L1NV9TfEm50p12WUxP/fs1fQKKu8aCuVDD0Ov4W5ln9PdkEWCmUHTw38JBoOBaxdD8bDKueBmltFAtDEFsyxwtcyplemGTFItjZoeanpY5hQhv3LooZWVVUM/P78Ha2QhGI1GdLpHZ1MKzZ7C+S/Yk5KSgo2NBbkXJU9JScfGxqZE9pw/f75I2v2oLEKU3zwmWYh7vqgvkeUAjRo1kocOHSod64pBSkoKr7XtwtQaXTGdnrX/RghpLX0Z9dbYB27TgyY1NZXIyEg8PDywtLQslTRjY2NJSkrC29v7gRT8zMxMXm7dnjkNOmJmcr4j4aGcDbDh7J5DzGr4bI48/v3KWczaNGDAsIL2hS8cIcTVooSLikpk156gewdUcbKKT5NSNiosjLrEez0hhBOwSQhRS0p5yiRIqZRRjSLxn9LDns90ZkadzjnKyr4b50h+IoA3ND28Lx6GHr74dAemVuuaQw+P3b5EaDVzTv19hIlVXsqRx3/fOIFdl+oMHDb4vs6p6aFGUSlCfuXQw6pVq8pz5849BEvzZ9euXbRs2fJhm3EHzZ7C+S/YU7duDRYu6kzr1rXuuP39dwhDB23k7NmSlY2iaveDbsLfVoepoP6NUN2vA6Zd4T4oe+FcV3/ndn9ksbGx4fF2Ldkcup8so7Lv682kGH6NO81Lr71yj9j/bqSULP3wA/o8157Fo4bQs2M71ny+nJJs9ZOQkMC4IcMY2a07cwcMo3v7DuzdU9A+zaWHXq+nbdfObAg5QIYhC4CIpHi+Dz9NvyGDqd28KT9ePIbBqCykFhYfzf+iQ+ny8otlbhsIkGZFP4qBVDZb3kXeuYWFldH83KHg8q6hUC708LF2Lfnh0sG7epgYw89RZ3mxPOjh4g/o06k9H40eTK9S1cOXmTfowephu26d+PbyHjJVPYxMieenqGP0GzqIOi0a80vYgTt6eCMxkj9TzvLCy93K3DZNDzWyKSS/NDQ0THh70lQGD1jDrl1nMBiM/P13CAP6rWLi2+88MBse9BfQLSgbZM9T//5o4r5BCLEI8EKZ2H9ASmkQQiSqE8r3A71RNu19pBn3zgQ+tfuEGT9swlwKHCpWYMay9/Hy8nrYppUpG9evI3L3dpZ2aIZOJzAYjSzc+i3bKnrSsVOn+0pzyri3aJoiaNa0DQDxaSnMnjgZ/43ryvx+DnljBCst9Ez++nv0RrBxdWbionkEBAQwcfq7LFn0IRO3bsMcgbO3B3OWf4Krq2uZ2gSABFGKC4gLIdyATCllnFD2f2qDsom0KVuAEUKIjShDyuLVYWSRQGUhRCBwA2XxnB4mcfIr7xoK5UMPJ01k2eJPmLppi6KHHq5M/3TRf14Pv16/jqjd/2NZx8fv6OGCn0umh1PHvcVjadCsmaqHqSnMfHsy/l89AD0cNYKVFp8x6+tNmBnA1tWJSYvnEBAQwKQZU/nk/cXM2vI1ZlJHBV935q/8SNNDTQ/LnCLml4aGhgmvdH8Fo9HIqBEzOHPmPNWqBTP13bn07tX7gdlQZg1QIcRXQEvAVQhxHXgXRXi/EUL0B64BLwFIKU8LIb4BzgBZwHB1SAUo+9GsAayBX9TjkcFgMLD+iw1s2fATmZlZtH6uFQOGvc6IsaMYPuYNjEYjZmbF63V9mKSkpLDqs+Xs/t92zM31PPtiV17t1bNIw7y2fr2BeU/URKdTRiGZ6XQMblyLOV+uua8KV0REBHGXr9CsSes7bo5WNjznVYkfv/2OoaPeKHaa9yIhIYEVS5ax9/e/sLSyokuvl/n+998AcuSjXq9n9IS3GDX+TYxGI7du3eLT95dw6shJnF1d6Du8Hy2eblnq9oEyxksYSnXwgiewVp1HowO+kVJuFUIMAZBSLgO2AR1QFsRJAfqpfllCiBHAb4AZsEpKeVpNN9/yXh4pT3q44Yv1bP16C1mZWTzdsTWvD+nP8LGjGPYv1cPVy5ez+3//w1yvp323brzas2h6+NPXG1jwZI0cejikSU1mrSuhHj7e6o6bo7UNz3sHlbEefsaenX9iZW1Nl54v88MfyiOXWw/HTHyT0RPG3dHDJe8t5eShU7i4OtPvjb601PRQ08OyId/8esg2aWg88vR4tQc9Xu2BlDLH9IkHRZk1QKWUrxbg1To/RynlbGB2Pu6HgFp5YzwaTHnzXSK2J9LO/lXMhTnH1h5g0D9D+eL71eh0un9VZctoNDKiX38aGyyZXqs5mQYDm77byowzZ5k2f+4942elp2Njoc/h5mhtRVLC/a14nZCQgItl3snQrja2XI+MvK80CyMjI4Mhr/WjmdGLiQEdSM3KYNOnP3D1Qijj3pmQbxwhBNHR0QztPohnzJvR0rkn0cmxLHvrY+LejqNztzJY6LCUe/yllCeA+vm4LzP5LYHhBcTfhlIhy+0eTQHlvbxRXvTw3fFTSN4dTS/35zG3NGffd0cY+s9gVn+79l+phyNff53HzcyY17gxmQYD327ezIwzZ5g2b94945eJHlpb53F3tbXlWlnpYY/XeSzLm/G+nUjNSufHpZu4evHyPfVw0ItDaJbxND3tBxJ7O4aPR31K3LvxdOnWOd94JULTw3JNQfmloaFRNB7WduKPzjJODwEpJQcPHmThrEWsWLaSkJAQ1qxcw3uz3mfv3r0YjYW/1a5fv87pXedo4fQsVmbWmOv0NHJ8AnHZin379hUa92ERExPDujVr+WDufHbv3p3jGvfs2YNnUibtg2tiYWaOrYUlPWs2JvTgUcLD7z3VLLBaDU7euJ3Dbd+VMOo1vb8tuQICArialkhKRs4VDvfcDqNpyxb3lWZh7Pjf/whOs+NJn1qY68ywt7CmV+WW7N32O7GxsQXGW7fyS5qLBlR3qYwQAldrF3p6dWHV4s/v+QzdN8ZiHBoaRSBbD9+f/T4rP1P0cO3KNSya/V6R9fD87rM859Uaa3Mr9DpznvRogs0Ns0dbD9eu5cP58/LVQ++0NDpWq4aFuTm2lpb0rV+fq4cP37ce7g0toR6mJOXRw39ulpUebico1Z7m3rVVPbThtUpPs+fnPwrVwy9XrKNB2mNUdqiKEAIXywp0cXqZzxet0PRQQ0NDQwMoxw1Qo9HIhFGTmNfvU26vF/w29xCdGnXh8ILTpP4gWTrsc8YMHlvoC/PixYtUNOTdRsAzy4+Q04/OCmvZnDx5koFdXyJp6x/4n73GT7PfZ+yQYRgMyui+c6dOU9XOOU+8ag4uXLx48Z7pD39rAp+eusbPpy9yMSKazSfP8+XFCPoPH3lf9pqbmzP87QnMPLSLA2GhXIi6zaoTB0j0rEDLp5++rzQLI+TEGSrbVszhJoQgyMaNq1cLXtTr7LEzBDv453CzMrfEIlNPSkpKqduJBFGMQ0PjXhiNRt4e/TYfD/2YrC1Z7Fu8lxcee56QJUcw+y2NVaM/ZezQMffUQz+zvPMQA8y8OXc6pCzNvy9OnjzJoBe7kb79V4IvX+SXhfMZN3ToXT08fZoajo554tVwdi6yHi45GcbPpy9xMSKGTScu8MXFSPoPu389HPb2BGYc2MWBa6Gcj7zNymP7SfB0LSM9PE1lm7x6GHgPPTxz9Az+NoE53CzNrNCnW2h6qKGhoaEBlOMG6J49ewjdGUlrm1cItKvBtfgQ+lm/QcUEH6o6VKeTUzcSD6Syc8fOAtPw9/cnyvxmHvco85sEVQ7MJ8bDQ0rJvEmTGV/rcdpXrkEDb3+G1nscx/AofvtFmdMTEFyJ0JS8w8MuJcXh7++fxz03vr6+LNv4LZlNW/Nzpi3mT3bk86+/w939/rc7a922LbNXLyeshh9/O+p4YuQAFn32aZkM5QuqVpkrKXmHsoWlxeDtXfBedpWqB3Mt6UYOt0xjFnGZCVy8eJHMzMxSt1UYi35oaNyLPXv2cGv3LV507UY1p6pciDrPGPeB+Kd6UsOlKt09O5FxLPGeenjTmHdhzxvGCAIrF32bjAeBlJIFk99hcuMGdKxejUZ+voxs0ogKkbfv6mGlSlxKSsoT92JCQtH18KtvSW/cmp/SbTFv3pHPN5ZcD2et+pyr1fz5y96MZiMGsWhZWelhFa6k5tXD6/fQw+AawdxIvZ7DLcuYSYIhXtNDDQ0NDQ2gHDdAd/z0B5XUaQMJmTHYGm1xNHNGZzQnPSMdgFqW9dn+444C0wgMDMSrnhsH4v8iy5iFURo5E3+UBM9InnzyyQdyHUUlOjoay9QMPOxzbhb+tE8Qf/6iLLLTomVLzol09ly9hFEayTQY2HL+JM5Vg4pU4QJwdnbm9YGDmf7eInr364eDg8O9I92DoKAgxk16mxnvLaRd+/ZlNo+s3bPtOU4Ex25fQkpJpiGLzZf3UbVZfdzcCt5Tt+eAXuxM38f1RGVYXlxKPB/s/4ys+AyWDf+Qri0789euv0rXWKMs+qGhcQ/++Pl36lrWASA2IxZ7YY2L3gkzoxkZ6YoeNrSrzc6ftheYRmBgIK61K7Lr9t47engk6hS3neMeST20Sk+nYi59ah3gx1+//gooeng2K4vdoaEYjYoebjpzBofg4PvSw17/Oj1sx0lxm+MRF+/o4Y9X9lC1Wb1C9bDXwJ7s0/1JeIrSKRefEs9nJz4hIy6TDwcspfNTL2h6qKGhoVHOKbcNUAdne9KMyQDodZakyTQAJMY7KxymGlJwcCq8wvDepwsJfM2d71jOxqyl6J9NY8XGzx65xTYsLS1Jycrb85yUno6dWikyNzdnydrVXA50ZfyB7Uw6+ge6J+ox+4NFD9rch4K1tTVL163iQpBgesgm5l7ehmfnJkyePb3QeD4+Prz/xWL2Vwzhw1trmHL0Paq6VeKdmiN5yb0DA51fZsFbc4iIKKVt37QhZxqljJ2TPSlZyvBIC50FqUZFD40mepiSmYr9PfRwwSfvUaGLH0tjN/BR5BckN4PPNnz+SOphcj5f4hLT07E10cOP16whxNOTUX/9xZt79mBs3JjZi8qRHq5fyaXKkpkXvmPBtZ/wfqExU+bcWw8Xb/iAkOCjrEn5lPcuzaSSS2VG+I3nWbsuvGTWlzlj52t6qKGhoVEGnD17lsWLF7Nq1Sri4uIetjkF8qD3AX1keKF7Z3KTQ6oAACAASURBVIZsGEOAoRo25nZYWzkQkn4SX3sf9Ho9mcZM9mf9zfyecwpNx9LSkrETRzN24ugS2fPT5h/Z8Nka0pJSqOjvzYi3x1GzZs0SpWmKvb09vjWqsS8slMd8leHBGYYsvgs9w7ixdytUzs7OTJkzK980bt68yScLFnD+xHF0enOe6dKVvgMHPnKVy5Lg7u7OrEXF30KsSpUqLP1yGefPn2dun2l0rnh3H2xbvQ1N9XXZtuVn+g7oVzqGakPJNEqRLi93YdQ3o6hiqIKd3g47K0dOJJ7Fz8kTvV5PhiGTXSn7mNWj8LJhaWnJ6PFjGD1+TIns+Wnzj2xcuZL05GQ8fHwZNuGtUtdD7+rV2XPlKs0ClK+ZGVlZbDx3kTEfjLgTztnZmSmz8yxGDCh6uGThfM6dOI6ZuZ62mh7eoUqVKixb/ynnz5/n3Vdn087+7rYzNuY21DY04uct2+g3oG/pGKrpoYaGRjlHSsn4CW/y5Rdr6PJCQ6Kikhk/fhxff/0drVs/egtwl9sGaGBgIKPnDGbRtCVUSPfBqqIZu+J+JtilCiGJLtzUXWfg5AFUr169zG357qtv2L70K4ZXaoGdrzXXEyKZMmgMi9YvJyAgoNTOM2XeHCaNGsOOg7tws7LhQlIsrw4dTO3ate8ZNzk5mdH9+tKvki9vtGlOhsHA17//xoKbN3l7euE94uWJ5ORkbHV5t0qw0VmSlJhcKucQgNCGkmmUIoGBgQydPpSPZ36MF16YuenZEr2DKhaVcI0M4ZohnP4TBj4YPdz4NX+tWss79Rtjb2nF1dgYpg0fycI1q0pXD+fM5Z0xo/nt7z2421gTEhfPK0OGFl0PX+9L/ypejGrfjAyDgY1//sKCW+G8PW1Gqdn4byc5ORlrkVcPrbEiKSHv/Nr7QdNDDQ0NDdixYwdbfvyWU2fn4uxsC8Cff57l1Ze7c+3aDSwtLR+yhTkptw1QgGefe5bWz7QmJCQEBwcHAgICuHbtGnFxcVSpUgUrK6syt0FKyfrlq5lQuQNW5hacj77OP1dPYp6Uxsx3prJi3dpS26PHwcGBT1av5Pr168TExODv789fu3YxefQ4XD0r0vXV7vj5+QGQmprK1h9/5OTBfVT080dvac1TFRxp4KssPmFpbk6v+rV5c+duYmJicHFxKZFtYWFhbP76KyJvhdPwiad4tuNzWFhYlPiaHzQ1a9bkijGc1Kw0rM2V50dKyZH0M0x6pnvpnESCMJROUhoa2TwqerhxxUrmNm6OlV7P2ds32XUhBJuEJGZNmcLnX3xRqnr48cpVefRwytgxuFb05IVXXsmrh4f2UdFX0cOWrvY08Lurh70b1mLs/0pZD2/epGHzp3i2Y8d/rR7eNAsjzZCGldldPTwjjjO93ZTSOYmmhxoaGhp8tXEdQ4e3vNP4BGjRojpVqnryxx9/0L59+0JiP3jK7RzQbCwsLKhTp86dnnU/Pz/q1KnzQCpbAJmZmegzJVbmFvxyYT/fHd1BfQt3nnetSfz+C8x8Z2qpn9PHx4dq1arx5tDhHF/xFS2TzHA/fpk3e/Zjz+5/SEpKYkjPV4n8ZSPPW6VS8dwBNnzyIRYy5zgnIQT+jnbcvJl3JeDicGD/fsb3ew3/q0d53jqZK5vWMKxvT9LVxU/+TVhYWDB62liW39rIvttHORF1hjXh31PnuUalOoRQ2/dOoyx4FPTQwmjESq9n88mjrNvzF00cHekeEETa8VPMnlJKjRYTsvXwreHDOLNuDe3JxPfiGcb37c2ef1Q97PUKUb99RWebJDwv7mPDkkVYiLx6GOBoW0p62BPf0OM8Z5HC5e/WMqzPv1cPx84Yzcak1RyJOcjZuFN8H7+ehl3qaXqooaGhUYoYDQb0+rxTQPR6c7Kysh6CRYVTrr+APgro9XqktTm3k2L56/IxplR/DnOdGSmZ6Qyu2Yav/znOmTNnqFGjRqmed9tPWwlKMtC9RiMAgiq4U93Nk7kzZ/NMl0487WRGlzpVAAh2d6GilTmTt+6mc527lQYpJRfjEvD1zbsXalGRUvLR7OnMal2XCnY2d8637lAIWzZt4qVXXilSOgkJCRw5cgRbW1saNGhQ6vOwMjIyOHToEAaDgcaNGxMeHs6VK1cIDAwkMDDnljtt2rWlRu2a/Lx5K8lJyYxvP7lIw/qKjLaYhsZ/FL1ej9HCgpsJ8fx+9jQLmrfAXKcjOT2D0Y81Y+WBQ2Wjh1u3UjUjjZ4N6gIQ7OZKzYoeTJ01k7adn6eVi44udSsrfh4uVLTSMXnLPjrXvWuHlJKLsYkl1sPFs2cwo0V9Ez2swPrDZ/61eti2XVtq1q7J1k1bSU5MYUqHiZoeamhoaJQyL7zwElOnjqV3nyexsVGG2x45EsrxY1d4ugz2ii4pWgP0ISOEoP/o4SyaOINadhUx15mRmpVBnCEVP7cAGhl82f/PnlKvcO3Z+QcdPANyuDla2eBo1LF7529Mb5izElHNz4t0o4Hfzp6ndZVgkjLS+eLYaZp3fK5EWwvExsZiY8y4U9nKpkWQF2t27SxShevHTd+x8fNFNA+2IiFd8sFtM2YtWk5QUOnsPXjk8BGmjX6HYOGJkDDy8mAqOrvT0KU659PCcKzizvipE7Gzs8PLywsALy8vBg4bVCrnz4sEqdW4NP57CCHo98YIpk+eSkMXV8yFjtSMTGIz0vH18eSJlCT27yl9Pdz3+066+vvkcHOytsZFwD87/se0pn45/Kr7e5NuzOK30xdoXa0SSenprD1ylic6dCqxHtoa0vPRQ2/WFlUPv/+e9Us/omlFJ5KyDLyflMWcj5eWqh5OGTEFvyxfkIJh14fh7uROHbs6hGaF4lLDhYnT8urhoOGaHmpoaGiUFZ06dWLzj99Tv84Uur/ahOioZL795gDLP1+Jra3tvRN4wGgN0FLGYDCg0+mKNU+pfcdniYyO5IeZS7mZHo+VjRV+vv7o9XpiMlOp6eZa6nZWcHcj8mIEfk4V7rhJKYnPSMPPLYiIxGQcbUyG3UmBu48fEVVrM3b3X9jY2NK5T3+ef+GFEtlhY2NDQloGUsoc9ywqKRkXt3t/Sbh69SqbV7/PykFVsFCHHoTeSuTd8cP44ttfSjxfLD09nXffeJshFTvjZGlPZEQEddy8WX1rG2dTL5CWkkr85Ug6bmlDgE8QFSt7MfvjeXh5eWFuXnbFS9tQXePfwP3oYbsOHYiIiuLn9z7genIiVjY2+HgFoDfXE5WeTqBr6euhi7s7EeHX8HdxvuMmpSQuPR3vAD8iE1NwMtFDKQVuPn7cCq7DmF1/Y21rS+feA3i+S8n1MD4tM48eRial4OLmdc/4V69e5btlH/Fxh8ewMFf08Gp0HFPGvMG6zT+Vih5OHj6ZHjav4mDhQMTtSIL1lfju9neEJISQlppO9PkYntncDn/vILyqejJ/6RxNDzU0NDTKGJ1Ox6qVa/nnn3/45ddfCAx04NixlXdG5ezdu5cNX60jLS2Nzs+/QIcOHe5ss/ZQ7H1oZ/6PceHCBQa+0ouXW7Sjy5NtWDhrLhkZGUWO37NXL2xr+JJVwZoKbm6EXw/nwPGjrD+ykxOHjhcrraLQrWcPfrgWQlJ62h23naEhVGvcgFf6D2LF0Qukq2PGpZR8dewcLTt0YtykSWzc9iurvvuezl27lrhCY2VlRa3HmvPTqctItRc7OT2DL05cpdtrve4Z/9etm3m5scOdxidAYEV7vG3SuHDhQolsA9i/fz/VzHxwsrQHICEuAQ+rCgTrvXHIsGSwR2dG+rzEaK/uEJNBpVAP2jZoRdfHO/Ni2678+ceuEtuQB4k250njkUbRw550b/kMLzzVmvdmzym2HloEB5LmaE8FN1duXr/O/mPHWP3P35w6crTU9bBrj9fYeOEySSbzLH87f5EqDRvxyuuDWHHoIumZJnp45Dwtn+3EuEnv8NW2X1n17fd0fqF09LD240+w9cylHHr45anLRdLDX376kS7BnncanwD+FZzw1GWVmh4GGgJxsFC+8ibEJeCqd8Vf+mObassrVq/S27EPr9u8TlZEFi4n/GlZpw0dG3Wly9MvsuuPP0tsQx40PdTQ0NAAlFFEzZs3Z/as2UwYP+FO43Pe/Ll0796Fip5R1KyVyTvvjKJPn5533jMPA+0LaCkQExPDxIHDGez/OL4NmmCURn7Zd4JZ70xlxsJ5RUpDCMGiz5cwftgoLvz6A+56Z9KkZHyDYYT+Fc60CVOZ80HR0ioKwcHBDJwykenzFuJhbklseiqB9eswZeZ0rK2tCR84khGffoyvnRU3k1Kp27wF48aMLbXzm/Lm5KnMmzaVEVv342ZrRXhKFgPHvk21atXuGTcrMwPLfCZdW+pFqUy6zsrKwty0mEiJQRoxR4eHhQs6BDohsDazoIVDPc5FhNLYsg5NrZviaebB4jcX4bTKmbp165bYFlO0OU8ajyoxMTFMHDSc4UGN8WvSAKM0su3gqWLr4XvLljFx5EhCf/sFb2s7UiW81/Flzp25xMxJ7zDzvYWlZnNwcDCvv/0OExfMx9PSgujUVALq1GPy9BmKHg54g+HLPsbP3pKbianUeaIF48aMK7Xzm5Kth2/8slfRw9RMBo6bVCQ9NGRmYpnPl0YLM7PS00NpordSYsSIDjM8zFwRCAQ6LHRWNNU35VzkJaqbN6RBVjNcEz1Z8MZinNc5aXqooaGh8YAICwtjwfx5nDg9G09PZZTPgIFP06ThNHbs2EHbtm0fil3lpgEaExPDhjXrOL7/GL5BfvQc2DvfOTEJCQl8OPcDDvy5DxA0a/MEoyaMLnT89JbvN9HSIQBfx7tDw4wZmWz+8iuO79mPd6VA/KoEE3r6Au6eHrzSv1e+KwC6u7vTsv0zeN1wprFrTVysnBBCEOjoy0f7vrqv5f3Dw8P5aP4cLp89hTAzo83zXXl90BDMzMxo1bYNLVu3Ijw8HAcHhxxzl57v2pVnO3Xi1q1b2Nrasn71Cno815bY6CgMUkfdRk3o0X8QTZo0KZY9+WFlZcW0eQtISEggPj4eLy+vAhfNOH36NEvnzyfi+nX01ta4Bway69hZ/G1ScHR2wtHZmeiEdM5FUGCFLTY2lsXzFnJ870HQCZ5o24rhY0dhbZ13v7omTZrwQeZ82mQ1xsrcElsHO+JjE9gff4Y3A3sCguSsNKzMrbDPtMKIEUczB5IzU7C3sKeDYxtWf7ySD1d8VOL7lAOtJ1+jBCh6uJ5j+47hG+RLr0H30MNd+0FQZD1s4+SbY3i/MT2DLevXc3L/PryCAvELrsyVkHO4VfSge78+Bephi3btqJkmeMq/Em629gghqOJWkXf27SiBHs4mNOQUwsycNp260i9bD9u0oWWr4uphNAYpqNuoMT36D34oerhk/gIiwhQ99AgKYMfuw3jrDDg6OeLk7ExMcioXk9IL18O5Czm27yAIQfO2rRg+rmA9fE+8R3NDcyzNLLFT9fB42nEGugxCGAWpMgVLMyusDTaAxBZHUg3J2Jnb80RmB1Z8uJqPV39Y4vuUA00PNTQ0NPJl+/bttH+23p3GJ4C1tQU9ez/Gz9u2ag3QsiQqKooBL/ajqaEmHZ2aEb7/FmN2juDdT2fRoGGDO+GMRiPDew+lZlQQb7j1BeDAzmOMDBnOyq9XFzi86vqVq9Syu1sR+ubEbpLiYplXvwPe/r7sCznN2u1rGft4LzIuZTJzwFsMmD6GNs/kzfTrl8OoZO9DBWvnHO5u5s5EREQUq8KVmJjImP59GFbXi7rPNybTYGTDvl+Zf+sWk6bPBJQx4z4+PvnG1+v1+Pr6Mv6NYVRLv87keo7Y6Rw4czuBz/f+zqqrF7jccwCv9Lz30LCikLvSl5urV68yY8QIxtavi3+VYFbv28exXTvxcXFj+sartKgRSXQ6HI52YsKsj/Id224wGBjZdwCtzb14uW4njFLy+/7TjB8xio9XLs8T3s7OjjemjeOjd9+nkUUVkPBD+HYismLZFLmD+PRkzIQZNubWhKdGYo418SnnGBvcFAAvm4r8enVXqdyfO0gBxtLZC1Gj/BEVFcXr3V6nYXpdWtu34ObfNxm1YyTTP5uZrx7WiApihKqHB3ccvace3rhyhbr2d/Vr4/E9pCRG80Gz1ngF+LHvzClW/r6SyW27kRGZxdxho+n3znha56OHN0KvUs/VA3e7nLrgaWN/X3o4dkBvRjTyoO5L9ck0GFm//2cW3L7J29NmAUXUw5HDqJZ+g8n1nLATDpyNSOTzvX+wKuwCoT0G0v0B6uG04SMZVasRfs2r88WBPRzb/ic+ji7M//0YT/hWIMZg4GSWBZMWLCpQD0f0GUgr4UO3ml0wSskfe08xfvhoPl71WZ7wdnZ2jJ45mg8nf0htWRvMJD8nbSNaRvNr0jaSMpMQ6LA2t+V2+i2EtCbZeIr6lo8B4GblxcGrv5bK/bmDpocaGhoaBWJra0tcXEoe99jYVOzs7B6CRQrlYg7ommWraC7r8JhHfRwt7anuUpmebp34YMZ7OcIdOnQI29sWNHKtg07o0Akdj7k1gLAsTp06VWD69Zo24njsDQDSMjPYeeEYHla2XIiPJCkpmeq27rwaWJ+jt0KoWsGPYcEdWTrvA4zGvN229R5vQEjKlRxuWUYD17Mi8Pf3L9Z1b/tpC89421LP1wMhBBbmZvRpXI2Q/X8TGxtbpDSuXbtG0tUQ2ld2x0ZIKtha82SQB08FVKBVgCs/rFlBampqsey6XzasWkmvysH4u7iQkJbGodBQFrRszsuVghn1ZGcyk6py8Lw1Q96aRYMGDfNNY/fu3filWfCYdxWEEJjpdLQNqE3Gldv5zpHKysrCwsqSLoNewalbFTxeq41/TV961GnAE75O1PCwJizrGnXcbWnnF4CVZRqB1h6sPfUF1xKvczbuHPWa1i/V+yBQNl4v6qGhYcrqZatpmt6ARhUa4GBhT1WnKrzk2JVF09/PEe7QoUPY3LagYYW6d/SwqVtD5LXC9bBu08Yciw4HFD3cfuE4njY2nIuLJjkpiZpOrvSuWouDYZeo4eHN+PotWbbw/Xz1sG6TRhyNyrmvZpbRwOXEuPvSw3Z+NtTzd7+jh32bVSHkQHH18Jyqh0Zc7ax5MsidpwIq0CbAle/Xfv7A9HD9ylX0CKyKn7Oih4evXGZW01a84FeNN57qjNT7cDzKyPDJ02jQsGA99E21pKmJHrbxr0NaaMF6aGltyUvDXsKntw+VBgcTUMuH5/2fop6jN4H2ztyU1/DXV6SpXT30ZkYq4sv3l1YRnnqNS0lnafBYvVK9D5oeamholEeklKxavYqzZ8/g7V2RF1/swsmTJ/OE69ixIwcPXOL330/fcbtw4RZrV+/mtR49H6TJOSgXDdDD/xyktkvO4Ueu1i7E3YrJMQH32rVreBgq5I6Op3QjLCyswPSfad+ey9YZ/HjhEDN3rcfPSYe9YwJXs66yYO9mJIIge1duJUYBYKu3ws5gQUxMTN602j3DLbckfg/fT2JGMuFJEay+tpmXB/XId0hUYVy9eJ5gV8ccbkIIAp2Kvll6WFgYlV1sSUlOxtbi7gfzKq623I5PorqrPZcvXy6WXffL1QsXCVZXBL4UFUUdtwqY6QQC8HJw4IW6dehRpxbn8imAd9IIDcXP0jGPu7+lU548Dg8P59UOXdk2fRVR6w5x8NsdbFq3ge4Vq9LWPYjm3sF08K3GmJpPEpmeyONuQbxZozUpMp4X3Z5mzekv+ZP99B8+oFTvQ2kvuiGE8BVC/CGEOCuEOC2EGJVPmM5CiBNCiGNCiENCiOaqu5UQ4oAQ4rgad7pJnJkmcf4nhLj3Mp4aZc6h3Qep4VQ9h1sFKxdib8bm1cOsvCvOFkUPL1plsfncUd7d8TU+tuZY6NM4n3CTmb//AggqObkQHq/on52FFY6Y5auHbdu146KFYMu5k8SnpXItNpqFB/+ia78+xdbDaxfPU9ndPoebEIJAZ5ti6qENKcnJ2JnoYWVXW27GJ1HjQethBXcALkdHUtPZDTOdQAd42jvSuWZ9ulevd289tMhPD53z1cOXnnmJb97cyIVPzrH7y7/4du3XtDNvwmPWtWjkXIsWTg3p5d6JBBlHbeua9HLpTrqI52nd8/wQupoT9n8ycGT/Ur0Pmh5qaGiUR+bOm8PiD2fh7ePI7r1v0/wpB1q3bklISEiOcHZ2dnz77Q/0fHU5bVotpHOnxTzeZDpz5y2gevXqBaRe9pSLBqiHtye3UqJyuGUYMtFZmOUYRla1alWu6fJWRK7I61SuXLnA9C0sLPh03WrOO2dR19uKoY1q0KFKEOMer0+nqp78cPUwZ+Ju4efkCYBRGknISsHe3p6YmBgSEhJypPXZ+hX49a7DN+JP/nE/z+BFY+jVr3exr7tq7bocC4/GYLi7+ITRKDkfnYSfn18hMe8SHBzMydsJmOv1ZBruvr2P3YynknsFriek4ObmVmgaua/xfqlWty4nb94CoIKtLTeSkjFKiRTcGV4WlpiMpZ0d8fHxRERE5PmqUr1mTc6lRudJ+1xyJMHBwTncZoyfTDfbRrwU8BRPBzRgZOVORF4MI8DSAXOhI8OQhZ3eioauPlxKisLewgrMob6rN2GZt9DZChZ/+TGenp4lvvY8yGIc9yYLGCelrA48BgwXQuTeaHEnUFdKWQ94HVihuqcDraSUdYF6QHshxGOq30IpZR01zlZg6n1cqUYpU9G7IhGpkTncMg2Z6Cx1efXQLDxP/CvcuKceLv1yDWccjNR2t2Vw/Vo8W7kSY5o2pGOQDxvPneRUdCSBLh6AooexGWkF6uGStauxb9+SxddD+CErnp7TJ/Na3z7Fvu4qdepy9HoMhqycenguqph6GJGIud6cDBM9PH4znmB3F64npD44PaxXh5O3lfxxsbElPCXRRA+VfLyeknBPPTyfFpUn7XMpefXw3XHv0ibjaZ51bU8zj8fp6foat0Ju4CUqYCbNyJSZWOusqWVTietZN7A1t0GYSarZBBMhwjCzg6VfLdb0UNNDDQ2NEpKcnMz77y1k05ZRONhb4+fnysg32jFyVBvee39+nvAtW7bkypUwxo6ZSZ/eb3H58lX6v17KH0eKSbmYA9pneD9mD5xKX6uu2OptyDJmseXmDl7o/2KOcLVq1cKmmgM7Qv6muWsTJJJdkXup2MC30AoXKPu3pSXEMLBNS5Lj44iOisHFypY2gX6sObad0wlxTHlqIEZp5Odr+6nSqDaDXu2BWWIy6YYs3CoFMXX+PFxcXLCxseH1Qf15fdD99xRHR0ez/aef+OfPI+jio3g80BO7Cq58eewKLZ7vWuRx3x4eHlRp+hTrT+6jhZtApxPsvBjBpbgMKqVn4hpcHXd393zjXrp0ibmT30KXEkOmwYijdyUmz16I633u49ejXz9GvPYa1ubmNPDxJtFgZNO5izxbX1lRcfXeA2w8epyg8+f55N0pODlWwLmiJyMmTaBlq1YANGrUiM/dbfj58lHa+NUiy2jgp9Bj+DWunaMSmpycTHToLYKrPnHHTQiBs6UdMXGxWBiVvc8NRiOpEsyFGVlGA0hBfHoqnmYWxCTEcXDfQQIDA+/regtDlOKcJynlTeCm+jtRCHEW8AbOmIRJMolii1qVk8ons2w/vXpk+yXkF0fj4dJ3RD9m9J9GD8uXsVH1cFvUb3QbnFcPbas5sDPkL56o0BSQ/Bm1B88GPkXSw9T4WPq3fpqk+Dgio2NwtbahVaA/a0/9xun4eOZ17IFRGvn+3DGCG9RlyGuvYJ6SSFqWAdfASkyZu+COHvYdOIC+A+//ZRkdHc32rT+y549jmCVG8niQB3Yu7nxx+BotnnuxeHrY5ElVD3WYCcHOS3f1sEKlwvVwzjtvYZYcS4bBiKNPEFPm3L8evtavH8NfVfSwvrcvydLIlkshtKvTABB8cWgP3548QsD58yyeNA0nJxdcvDwZOfktnjbVw4o2bLt8hNZ+tckyGth69QgBTWrl0cOIi7fxd33ujpsQAkdzR2Lj4jCTOoxZRgzSSLrMwAwzDKoeJmYl4ywrEpsQw35ND/PE0dDQ0CiM0NBQwsLCqFWr1p11D0JDQ/HwcMLf35VroXfDtn2mJiOGfp9vOtbW1nTq1OlBmFwkykUDtH79+gyZ/QZL53+CWSqkiQw69+5KnwF9c4QTQvDB8sV8uepL1v6wCZ1OR8fBz9Gj92tFOo8QAoM04lyhAuZ6PdFRUaRnZJJha4Gbnw9Lr/xCijGDZs+04MSff/BW7Yb4OCmLdRy5EcZbw4az4qsNJd5LTkrJ+OFD6e5hx9iBPfj60HFm7DjJ9eQ0Ji/8kBe6dStWeuOnTOeHb7/ho7UrCL1wBGsrK1wruBLqWpl3J03ON05qaiqTRvRnWltfAt2VkUaHLkcwYeQgVmz4/r6u0d3dnQ/XrmXVkiV8uWcfjtVrckrA7gPHiItPwD4zleWdWmFrAAdLSz47ehp7vS3Lp8/By8eHKlWUeU4frfyML1etYd5P2zDX6+nYsysv93glx7mEEMh86gd1PKqy5cppOnlWwdbMioTMdH67cYFmroHEpCeRnGnkUkIcHo5Qz7k+3y38Bh9/H5o1a1bs6y2U4lVdXIUQh0z+Xy6lzLviEiCECADqA/vz8XsBmAu4Ax1N3M2Aw0AwsERKud/EbzbQG4gHni6W1RplQv369Rk2dwRL5y1BxAvSdel0ef0F+gzI+VXRVA+//OEHdDodHQY/x2tF1EOdqocuFSqgt9ATGRlFemYm6VaWuAf6seDEXyQbMnmszdNc+ut3pjxWA18XZTjowas3GT9iKJ+v31gqejhh5GBeC7Dg7dFd2bDnFNN/O0NY4jHemb+4+Ho4NR89dK3AZZeqherh28MHMOWpQALdAgA4fOUW44cPYuXG+9fDxV+uZeUnS/jq4N841qrOOQH7T+8jGWuPzgAAIABJREFUJj4e25QsFj31PDaZAvta1qw5dxgHoxPLps7HO4ceLuPLVWtYsOUX9HpzOvYuuh5Wr1CTnbEHaGFdH0upJ9WQyu74E9S2rEWcIZ5UQxZhGbdw0Fci0LYRq2Z+j5+mh5oeamho3JOEhAR69+7Bnj17CK7sxZnT1xgxcgQzZ8zGy8uL8PBoYmKSgLvTUY4euUJgYN4V7R9FykUDFKBNu7a0fqYNKSkpWFtbo9PpSEhIYM1nK9jz+y7sHR3p/npv2jzTlv5D+tN/SPG/PrZ67nl+/Pt/vFKvJvYODtg7OLD19AV6DBzEqLfGk5KSgqWlJT/9uAXrU2fuND4BGnj7svNQGBcuXKBKlSolutaQkBCcUhNp4KekM/DJpgx8sikbj57BzNy8SJWd8PBwVn+6jFOHD1PRy4vXhgzmm207AEhJSUGv16PX6wuMv3PHdp72tyLQ/e78okZB7mwLCeHMmTP5brtQFLy8vJg8e3YOt4yMDEb268MwfzfSbt/Gyc4BEPStU503f99Pv6at+GbtF0yerax0aWVlxcBhQxg4bEiB57GxsaFiZT9Coq5SzVVZ7ERKSbJeYqgayIyd/2fvrKOjOvoAet9aPNm4OxqCBQ1WKFbcHYIVp0gpXpxixYsGK+7upUiB4gQNmgAJhDhxz+77/tg0kAZJINjXvedwoLNv5G13787Mm/nNcdz0TXkYG0l8ehruhhaciXhEQMIL7HQduaeOoEPR9sSlxbF+ybqC7XCJ5DfqY5QoiuXfdZEgCIbATmDIv2brNdWK4m5gtyAINYApQJ2sdBVQRhAEZdbrnqIo3s56bSwwVhCE0cBAYEJ+Gq7l41C3fl3qvMaHa5av5Nzx0xiZGNO+Z5cP82GTJuw7+SftSntiZGSMkZExB+8+oGPvXjl9uG8vJgHXsgefABWcbTkadK3AfGiaEUcF15IA9KtTjn51yrHx4sN8+fD3ZUu59Y8P+/TNtw9r2uvjaqnMTivnYsORh7c/2IfjpuX24UCf7nQ1dSX5eQQm+po6OxYqzXi/k3QpW4+tv69n3DRNJPS8+tC+uAMBgQEUMtYszRVFkTS9DPS9LFlyeAcOEiuCUp+TmJmCvcyBaym3eZoeirnciSDdIL6170xSRiyrF63X+lDrQy1atLyDgQP7YmaezJOnc9HRkRMWFkvjBvNwdy9M927dad+hA927rmTQoOGIosiJE/5MnriPHTv2fu6m54n/zAAUNLO4/5xfl5KSQt+OXakutWKMe03iUpPZPO03nj5+Qvc+vd6r/C7dezDm5g0mnbpECVMjHsYnkWZmza8/DMpRd1REOFa6uQNoWOnoERUV9cEdrujoaGz0dXOlW+vrEhn+7mAb4eHhDPbpSkcnF3wqVOZpbCyLRozEZ8xovq1TB319/Xe3ITISW6PcHTJbQxlRUbn3HH0ICoWC+NhYLDyceS4IaOIigp5MBqKItaExJ8PC813u+FlTGNy9H5cfB2IhMcQ/9RnlG1SnRYc2TH82lKa2pZAKAhlqNRIErt65hZ4indaF26LU0XT6pLqmRIZHvqOm96CAF28JgiBH09naKIrirrdWLYqnBUFwFwTBQhTFqFfSYwVBOAV8B/w7TOom4CDaDtcXw7992LtDNypnODLUtgFxaUlsm7SU4MdB9Ojzfktfu3TvwegbN5hy9gIeJsY8TEgi3cKS2f/2YXgE1vo6ufLb6OkUmA/tDBS5yzdUEJVHHw7p6kMnFye6Va7A09hYFo8cQZfRY/LlQ5vXtkH+UXwYFxuLuZ0+qa8MrnVlckRRjaW+CedCg/Jd7qTZkxjoM4Db0XdQqk0I5BHeLarQqlMrxt4fSzXdakiQkClmIiBw7cENpHrWfGPfCUO5xocSuTl3wwr2fgGtD7Vo0fJ/RXx8PHv27OPJ03no6MhJTEwlMDCCfgNrscJ3Cd27dWfhgkWMGj2CO/7PadP6Z+zsbFm6bCVVq1Z9dwVfAP+pAeirHDpwEE+1MbXcNTPPlgbGDCj5LeM2bqO9T+d8R1gEzQ//7MVLefDgAQEBAdRwccHDwwNRFLly5QoXzpwmNCKSzLRUAh74U8XFDWlW8ByVWs2NmCgGeXp+8L15enqyODIGH7U6u3yAc6HRdPd+9wdzw+rVtLF3oqKTCwDOpmaMrFiFKfPmU6t27Tw9MShfqTKrD2+gfumXaWq1yMWniXQqWTJf9xMTE8ORQ4eIj4mlyjc18PT0zNUGL+8qXAp+iL0goMq670cxsZgZGnMhJIgKLernq07QLHHbuH87fn5+RERE0LNMGezs7MjMzOSFmIa9kTl68pedyn0Zf+NsUiJ78Alw64U/3k29c5UtiiI3b97k/F9nUVqY0aBRw/w1rgA7XILmzVwF3BVFce4brikEBIqiKAqC4AUogGhBECyBjKzOlh6apwAzs/IUFkXxn7McmgL3Xle2ls/PoQOHKJ5qRnXnUgBY6JvwfeEGTFu3nQ4+nd7bh3OWvPRhzdf6MAJVehrHbgVQvZBzDh/6RcbQt4B8uOR5PD3+5cOzT2Pp0uPdPty4ejXtnB2o5KJZCeFsZsboKpWYOH9evny46sAm6r2iPrVa5FJIPF3e14cvYqlS8/U+LFfFmyt3g7GRvPThk/gXmOubcCXyEZXa18xXnaDx4ZZDW7N9+OMrPkzUScRKzwod6cuJhOPSE1jrFsoefAI8TrpJtVaVc5X9jw/PnfobU0szGjRqkL/GaX2oRYuW/yPi4+PR01NgbKzHbwuPMnniLgoVtiEoKBJVJkRGRmJpacncOfM5efIkjx8HY2pq+sFbVj4l/9kB6B2/65Q2yxmNTyqR4KSn5Nmzt0e9fRdFihTJnrVXq9WMHjKYxPt3ePj4EZVszXAzMSRRkU6njSsYX7cpggA7Au/TpEvntx48nleUSiX123Vi8q6ttPVwRVcu5/DDIAyKlqBkHjo7d69dp7F70Rxpxrq6kJJCRkYGCkXumfx/4+HhgaG7F78evU7zUrakZmSy8cpzarfolK/D469eucK0n0ZQ29IepY4Oy/YdwraiF2OnTM7xRevWpy8Du3Smpoku9vGJRCelsv1BEKWdi+JHKsta52+f1z9IJBLKl8+5Wksmk9F7+BBmT59PM9tSmOkZcj4sgER3JSFhAVhFW2Grb0NAYiC39e+xuu+aHPnVajUTR40l/NJdKho78SwzhW5LV+e9UflfcvYuqgJdgFuCIFzPShsDOAGIorgMaAX4CIKQAaQA7bI6X7bA2qx9TxJgmyiKB7LKmCEIQlE0hx8EAW9e46fls3L7ynWKGeeMBCuVSHDQMfsoPkx6cIcHTx7hba/E1VSfRHkGLZdv4pemdREEgc3+j2jY0afgfNi2C+P2b6JjGUd05TL23wlB161U3nx4/RpNi+aMCGusq4uQmpovHxoVLsuc4zdo6ulAWkYmm68/pU6r/Pvwlx9HUsvMCROFDkv2HsG+clnGTpmUw4fd+/WhfycfvGUmWCfGE5OSwr7gB5R0LsZtnXh+aFOwPhwwZgDLxi+jmqIaJnITbibfROYlJSrsLoGJ1pgr7Hie9oAo61v82m9VjvxqtZrxI8bx9EwgJRVFCFUHsOG3tXlvlNaHWrRo+T/Dzs4OExMlM2fsZ/XKU1y8MgU3NysyMjIZOXwLXbt24tChPwDNaqb8/I58KfxnB6BOhdx44n+eIuYvj+ISRZHnKXFYW1sXWD1HjxzBMOQJ6ow0BpUvTkUHK9JVKsqnpnPJIYHfHt6kfIWK9J01HS8vr1z5ExMTefbsGba2tpiY5D6v7U1069WLkl5eHNi2hdSUVL7tM5jaderkaXbE0c2VwOgoStvZZ6elZmaQLpG8dZ/TqwiCwIRpv3LixHG2Ht6LXK5Dp9HDcHd35969ezg7O7/zqYparWbGmJ8ZV64qZvqa5XpVXdyZf+Ucly5dolKlStnXWlpa4rt1Gzu3buXwmdNEJahwqVyV0t/VZXyLFu/1BOdtfNeoAS7uruzcsJmrUVFU6dOC0U0aExUVxZZ1W7j20J/SlUozov3YXJ3os2fPEnf5IT941M1Oq5Dszp6zf+a5fkFVoFEfz/LPuuU3XzOTrJn8f6XfRBOk43V53q+Xq+WT41LYjafXrlLI9OV3XhRFQtNiCtyHRs+fIGamMsS7MJUcLUlXqajglM4FRxtmXQugQsWKfP/L7AL1Ydfve+NZthwHdmwmLS6VWt1/onbtvPnQwdWNgKgoyti/4sOMDNIFIX8+nD6LEyeOs+PQPuQKHTr/PCj/Phw9jlElv8FMXxO119upMIsunn6tD1du38LOLds4c/o0kS/ScatekQoN6tKsZcH7sEHjBrgWcmXb2m2ERIZQ+7vaNGraiKioKDb9vpVHD67wjXcp2nT46bU+DD8TjI99y+y0UmnFOciRPNev9aEWLVq+NtRqNXv27GHPnh1IpVLatetE/fr1EQQBiUTCnDkL6NatI7PndsTNTRNdXS6XMX1mO1wcfyQ4ODjPR4h9ifxnB6DNWrWk58atuEZbUNjclnRVJnsDr1GmZtUCmXX/h1OHDtLK3Zlph+7zUzl3ABRSKYJaRdPSHpz525+Js2blyieKIst+m89fB3ZRxMKQwOhEytaoy4+jf84+8/JdlCtXjnLlyuW7zZ179WJsz+8ZZmiInbEJyenpLL92hVZdffL1eF8ikVCnTl3q1KlLeno608b/TKDfZRyNDQiISaRpl6507tb9jfmfPHmCrUyRPfgETUeutr0zJw8dztHhAjAxMaFH79706N073/f8PhQrVoyxUyflSLOzs+PHUT++Nd/Jg0epYZVzX5u5vlHeKxaFgp7x1/Ifp1nrFvTYsA2nGCvcTe3JUGVy8NlFvL6tUsA+PEDrQk5MO3SPEd4VgJc+bFa2KKdjHzBhxq+58omiyPJF8zl9eAdFbfQIiEihdNV6/Dhy3KfxYa/vGWFoiJ2JxofLrvjR0qfrB/pwHIFXLuNgZEhgXAJNfXze6UNriU724BM0Pqxp48aJg2/wYZ9e9HjPmAb5pVixYoyfnvNoSzs7O34aM/St+Y7vO0ZFw1I50kx18j65oPWhFi1avjZEUaR7dx9u3bpI7741yMhQMWRIbxo1bsWc2fMAaNKkCc7OLjg55zyqS0dHjo2NKVFRUdoB6NeIUqlk7hpf5k6Zzmq/ywgyKfVaNKHXgH4FWo+uvj7J6fEIgkCmWkQu1fxQiiKkZapQ6OQOvgGwb89uws8dxLdNOSQSAVEU8T1zjvVrVtG158ftULi7uzNq3lwWz5xFrN9lpHq6tO7WjVZt2753mUvmzcUm9BE/1NdEP1Sp1fyyfSNOrm7U+Oab1+bR1dUlJTMjV3pyRjq6Bu8O/PGlomegT0rGiw8rRHuCnJYCRKlUMm/tMuZMms7Ge2eRyKXUb9WYXgMKdpWgnr4ByelxCOT0oZp3+HDvLiKu7mFNT49sHy499hfrf7ela4+PO+Hk7u7OqDlzWTJrJjFXriHVKQgfzsPq6WP616kOaHw4feum9/ahnoHBa3J8HegZ6pOSmfphhWh9qEWLlq+IM2fOcP78X/jdmIyenmYbR8dOVShRbDTf9+xN8eLFAfiuQWO2bblEzZoeJCamcvDANe7de87z5y/w8PD4nLfwwfxnB6AAzs7OLFi57KPW0ax9B3xHj6CiizN77wfR2sOVxPR0pDo6bLr5gCbtur42397N65hUrTASQSA2JobYF9FUtxAZOncmtes3wMHBIfva+Ph4fH+bz5WzfyGVyfi2UTN8evbK8/Kw11GmbFlWbNn83vlfRRRFzhw9zLIGLwN+SCUSupcpxu/r176xw2VnZ4dopuRueCjFrTX7ddMyM9n/9BETJ4wskLZ9Dpq2bcXko0MoYemEXKr5Ct6Nepq/QrQz/loKGGdnZxau/rg+bNq+AyvGDKeiqwu77wTTtqQLSWnpyBQ6bPQLpHHb1z8B3Ld1LdMbuiCRQGxsDLEvoqjpmMGABdOpXa9hbh8ums/VcyeRSuXUatgcnx4f7kPfzVveO/+riKLI6SOHWFL3pfekEgldS3mwbt3bfYiFCfcin1PMUrN1JC0zk0OhD5n8y9tXXXzJNG/fgrEHRlDE1B25ROPDh7GP35HrX2h9qEWLlq+Io38cpW37CtmDTwCl0oDmLcrxxx9/ZA9Ahw75EW/vCjRtNIfzFx5QzssVE6U+KlUmM2dNZ8L4SW+q4ovnPz0A/RR4eXlRrUMn9qxdQ2JEOAcCnlLI0pQXOgaUrP4tLVq3fm2+lOQkjPUUREdFkhb/AntDPSRGCkyEdIZ934Wlm3ZiZmaGSqViUE8fmtpJ6d2sFOkqFVsvHWJSwEOmZj3G/9yIoogUEYkkZydBqa9LQnzYW/P+smA+w/v2x+zZY0zkOtyOi8Zn0EDc3d0/ZpM/KsWKFaPVDz2ZvMiX4gZWxGamkmSSz86xqO1wafn68PLyomr7zuxdu4aEqEgO3A+hsJWSaLkRntVrv8OHVkRFRZGWEI2DiQ4SQYZSmspPfTqxZP3ubB8O7uVDC1cVAzoWIz1TxZYLe5k89gFTZs3/xHf7ejQ+JJcPTfX1SIwPeWveaQvn8VPfAShDA1HKFPgnRNF1yICv3ocdfurC4vmrcZM5kKBOIsMqn4VofahFi5YvkNDQUI4cOYKuri6NGzfGyEiz3crY2JjHjxNzXR8RkUilii+3vVhaWvLXX3/j6VmczdsGUreuJnBeZGQ8VSpNoeY3336aG/kIaAegn4DO3brTtGUr7t69S2ZmJjKZDDc3NywtLd+Yp1yV6py6ex1XeQrOpgYIwPbrwSARMUqNYOHc2UycOo2//jqFh1469UoUA0BXIqNr5SIM23+dZ8+e5Xgy8Cbi4+PZt2sPj+4+oLhXSRo1aZKns+3yikQiwcLRmcDIF7hbvozUdeJhEN61a781r7W1NWt37eD+/fvEx8cz0tOzQNv2MRFFkQsXLnBs758YKg1o3rY5bm5uALRq34YGTRtx+/ZtlEolhQsXZp1kax4LRrNmUYuWr5D38aFX5W84cesCbgaJuFroIgBbLoSCFIxUoSycN5uJUzQ+LGGUTP0ymqi1ugoZ3Wq4M2Trtfz5cPcenty7T9EypT6KDy0dnXkUFY2bhXl2+omAx3jXrvPWvNbW1qzbtT3bh6O/Qh8e2vMnRkpDWrdrlu3D1u3b0PBfPtwgyeMKHK0P/9MIguAIrANs0HwSfEVRXPB5W6VFCyz8bQETJ4ynXv3SJCWl8cMP/dm4cQv169enY4eOlC07gx7f16BcOVcATpzw5/Rfd1mzumWOcm7dukUJT6fswSeApaUx/QbUYuOm9XTs0PmT3ldBoR2AfiKMjY1zBYl4Gz36DqRPpzaUFCKpXdiKNZcCic5MYHhzR0Rg2alNrPa1RxQleJjn7oAUt9DnyZMn7+xwPX/+nB8698Rbx4kSRpY8uHGM7ms2snTT7wUa1nnY+ImM6tuLRo4WuCiN8QuN4lamlEWdfd6ZVxAEihUrVmBt+RSIosjPw34m6MRzPGVliFMlM2jLEPpN7kOjpo0A0NfXp2LFiu9ZvnbGX8vXS7592GcgfX1OUVovnPqeZqw4HUxkRhI/ddZE6F1ycB2rV9ojqgRK2Ojmyu9hrZdnHw7u2p2aJtaUV5pzd+s+eq7dwOINawvWhxMmMKpPbxrYWeKiNMEvLJLbosDiLl3emfdr9eHIH8dx/Y8QrClDujqZA5uHMnxqb5pofajlw8gEhomi6CcIghFwVRCEY6Io3vncDdPy3yMyMpK169Zy+fJFTpw4xtXrk3Fy0gQROnfuAc2btOfJk2AcHR1ZsXI1Dev3oISnIxkZKh4FRrB9+65cEd7T0tLQ188dH0FfX0F62gfun/+M5C184P8Zjx494ty5c7x48YGBYD4iZmZmrNyyi2PPVey+H0t0ZgobfyhBtWKmlHLWZ0GvYpw5tAFTS2tuRyblyn8nKglXV9d31rNg2q+0MS1NfZcyFDG3p7FrOeooXFm5qGD3grm7u+O7bSeS6g24YGRL0Y49Wb5xMwYfGDxDrVZz8+ZNLl26RGrqh30R/ynr4sWLucpSqVT4+flx5coVMjJyBwL5N9euXePRyWCamrbGzagQJZSlaG/cjUVTl5CWlvZB7dQ0Vsj7Hy1a3sLX4sMVG3dz9JGEHdeTiExPY9M4d6qXMqZkIV0WDnPlzB/rMLW04VZoSq78/mHJefLhwhmz6OJQhEZFSlDMyoYWxUrRWGnLqiVLC/R+3N3d8d2+A1mt+lxSWlG8S3d8/899eO1YEKUN2mBjUBgno9J4Kboxe7LWh1o+DFEUQ0VR9Mv6dwJwF7B/ey4tWgqea9euUbKkB/7+h4iOvkuPnjWyB58AVaoUobJ3YQ4ePAhAi+YtCA4OYdTImUycMI+goGfUqlUrV7m1atXi8qUA/P2fZaelpqazasVZmjZtmev6r4X/1BPQhIQERg4YhDo0CjtdQxbER1KzZVP6DvohX+H0PxUmJib0GjSE/St/o1l5M2RSCUlpGcSkqnCyNefb4qlIpVLupety1D+IOsUdSc9Us/lqANbFy2Jv/24HP7x1h07Fm+VIq2hXhCmnDxX4/ZiamuLT/c3HDOSXR48eMXrADzhJdNCTyJiREM3An0fzbZ23L2N7HY8fP2ZUv8HYZeqgJ1UwPTmcgeNGUqdeXW7dusWY/qOxTbdGgoRn0ueMnzeBipXePFt/+thpiuKZI00h1cFW7cidO3coW/a1R8XlDVHQ7nnS8sEkJCQwvN8QMoJjsJabMCcllNptGtFvyMAv1offDxjKgXULaFbdGLlMQlJqBjHJapxczfm2TDhSqZS7iQYcuR5M3VIOpGeq2XTuCVZFy+XJhwG3/elTKee2AG9nV0ad/qvA7+dj+HBEnyHYpBuhK5EzNT2UwROHU7vu+/lwWK9hmCYYoyvo8EQMZsjkH6lbX+PDYX3GYpBgh4BAvN5TfvltPJXe4sMTx85griqZI00u0cEwVetDLQWHIAguaM5hvfia13oDvUGzr+7UqVOfsmlvJTExUduet/C1tOfevXssXjwHc3NDnj6LRi6TcuZUzjOXW7XsiVwuz5H/n3OZz58//8Y6V61aw8k/g/G7nEhKSjqpqRl06zYQlUr1xb0/eeU/NQCdMWESldN0qVq2JgBqUc3Cfcc4XboU39Ss+Vnb9ia6dP+eZ8/Dueu3hqDYFHT19HF0cUAmkxKdBM6mpixcuZZVS36jz74TyORy6jRpSf9uPfJUvlQhJ02Vga7sZSSuhLQUDAy/7LD+arWa0QMGMdClJI5KzdK45PR0JkyehmepUlhZ5T2KhVqtZlT/wfSwLIejiWa2KjkjjemTZ1GkWFFG9x1Fe71WmBqbApCQnsDEQRPYdnw7hoaGry1TaaEkRAzMlZ5MYq7lFe9FAR68ruW/yfRxUygVbUqlQpqjkdSimpU7j3C6bCm+qfn6SKyfmy7de2l8+MCXx1Gp6Orp4+Bsg0wm40WCBGdTUxasWMeqpQvpuekEMpmc2o1bMz6vPpTLScvMRPeViLnxqakYvOF7/qWgVqsZ0XconQ2rY2ek2UubkpHGvHGzKFk6/z78qfcwGqnrYmtpA0BqZioLxs6jaPGiDO01hvIpHTHS+ce7CYzqP4n9f219ow/NLZSkiwG50tMlWh9qKRgEQTAEdgJDRFGM//froij6Ar4ARYsWFWt+QX2+U6dOoW3Pm/ka2hMdHU2zZo2JiF6CTJbCiROPGdhvDZeuTkWhkHH06E0eBUYwacJu7t8PwMbGJt/1Hj9+nFatm/PzuKa0aVuJR48iGTFsMUOHTqBx48YFdHefjv/MEtyMjAzuXfGjmL4pgfcfEnjvAcGPgmhoV5i9GwsmvH5e27Fo7q+0qf8N7epV48d+3xMcHPzG6wVB4KcRIwjKdERmYo+dgwNyuZwnYYlcDBLw9vbG2NiYoaPGsuXQcTbsPUK373sjk+WeW1Cr1axZvoJW39anVY3a9Onkg1cNb3Y/vowoag5SU4tqtj26QKtunT7ae1AQ3LlzB0dBnj34BNBXKKhr5czRQ4fzVda9e/ewztDJHnwC6Mt1+Na0EL5LluKS6YSpjmn2a0YKI4qri751xqlxs8bckvqRmJGQnfY4MRCZvZAdeOOD+GfWPy9/tGj5FxkZGfhfuk4huSWBDx4ScO8BwY+eUFvpye71eQyGVUDtWDTvV9p+V4N231VjWP+eefDhSIIS3JAZOmLn4IhCIefJ8yQuPpC/9OHIn9l84ATr9xx9uw99fWlTtw5tatWkX5fOlKlWha33buTw4YY712jZ9d17Mz8nd+7cwTrdIHvwCaAn16GqQXGOHsy/D00SjLDVf9lB0pXpUl5aluWLl6NMcMVI/op3ZUZYJZZ4qw+bNGtElJ4fKZkvfRieHIDSSetDLR+OIAhyNIPPjaIo7vrc7dHy30Mmk6FWi6SnZwJQq5YH39QsjmfxETjYDmD61L38+cctQGTFSt/3qmPX7h0MGFiXoT82xMHBnBo1irFn/2AiIiKIjY0twLv5NPxnnoCq1WriXsSSbBqHrY4JgiCQoc7kfngE0Ya59wx9LKaNH4N97B1WtyuJTCrh9tMoRvTtxoqte7PDM/8bhULB1LkrmTj6Byx1oxBFkah0A6bOXZmvs+2WzFtA2LHzTC5ZGx2ZjODYaBYdO0EJ74pMPrcHe10zgpOjqd+uGU2aNS2oW/4opKWloSOR5krXlUlJTUnOV1mpqanoSnJ/FXQlMpKTkpGrc7+mEOWkpb5575KFhQWTFo9n6vBfME40JVVMwcjFgLlL5uSrba9FRLuXScsHoVariY+OIYl4LOWmSGQaHz4Ke060NPqTtWPahDE4xN9mdScPjQ+DoxnZvxu+m9/uwym/rmLCzwOxNAxFRCQq0Zgps1bly4dL58/nxZm/mF3NGx2ZjKAXL5gFc9GYAAAgAElEQVR94jjFK1Vi1Pk/cTQ05klCLHVataBx0y/fhwoht6d0BDkpyfn7fUtNTUUh5H4f5YKc2MRYJKrcr0nVClLf4cNZS8czbtg0JMlKMknFuog+i5dpfajlwxA0+wVWAXdFUZz7uduj5b+JiYkJtWp9w8wZB5k0uSWCIDB3fme2brmA78rvad1GE3QvPDyOat5TCQsN4/jxPwgPj6R69apMnjyNMmXKvLWOG9evMmVa/RxpNjZKdHRiCAgIoHz58h/t/j4Gn2UAKgjCUOB7ND8dt4DugD6wFXABngBtRVGMybp+NNATUAGDRFE8+q46wkPDGeAzkJIVStKuc1tMTEyIjIslPj0NC11Nx0YukeEXGUKGwcunW8nJySxbsIALJ0+AANXq1qf3wIHo6uaOrJhfoqOjCbp5kRFtX37IPB0taOAaz6H9e2nXsTOXLl3iwPbNpKem4ljUA//L54iJCMPIzIJeg0bj6OSMIAg4OjrmaZ9WREQEv82cx/WLfjwLvM+Suh1RZD0NcFKa08ymCLHm5qw+uIOwsDAcHBxyhfXPyMhgzXJfju/fjyozk3LVqtJv6FCUSqXmUPXTpzm8fQ+iKPJdq6bUrFULf39/dm3YSFxMDNXq1aVRkyacPnWKjb5LSUmIx8bRmT7DhmcftptflEolJx/epZquGZZKJaZmpggSgRPhwfxcd1S+yvL09ORhWjSJ6SkYKjRr8dWimtMxgQyeMJVxF8ZSQ1UNhVSzTFmlVnFbvMOQ6j+9sUw/Pz/2bNqLSyFXSnl70qBhgzwdAZFntDP5/zd8Ch+GhYbTr8sPlK7o+dKH8bEkmKRiptAsgZRLZNyIDSLD/qXrkpOTWTpvIeeOnwKgxnd16TNoQMH58NYFRnZ4uTfQ08mcBu4JOXx4cOcm0tNScShUgjt+Z4mJDMPI1IJeA8a8lw8Xz57DzcuXeRr4kN9bNkdHqvGhs5kZbV2dCbUw56d9u97uQ9/lHN+/H7UqE68qVek39MccPjyycxeiKFK/RfOXPty4kfgXL6har95LHy5fRnJ8PDbOzvQd9tMH+fDvoBuUTXPAwsQMU3NTBEHgfNJ9ptTvm6+yPD09ecpzkjOS0Zdr7l0tqrmeeYvRPccy7Nx4MtQ1kUuyfCiqCNW7SfXqP7yxTD8/P7Zv3ItrYWfKVylJQ60PtRQcVYEuwC1BEK5npY0RRbHgA1lo0fIWli1byXff1eHIocmUKu3AoYPXsbU1yx58ApiaGmBsLOfvvw+xem133Nys2LH9IvXq1ebs2fMUKVLkjeW7uRXC7+oTatR4GQU9ISGFtLRMHB0dP+q9fQw++QBUEAR7YBDgIYpiiiAI24D2gAdwXBTFGYIgjAJGASMFQfDIer0EYAf8KQhCEVEUVW+rRxWrpszjCjzyD8Bne1d+W7cQZ1snlt2/greFPXb6xvi9eE60SoWhTBPeWBRFhvTqRVU5zP/GG7UocuTGVX7q34/fVq3+4MAcoaGhuJjlPjKlkJUhFx4HsmrZUm4e3kbnMk4EpcSzcsl+RtUvSYUapQmLTWLmjLF0HT2NqtWq56m+5ORk+nXsSQNZabxtG7EhPIHUF4mEZ2ZiY6tZXuVqYsG+wMcYGxtjbGz82nImjhyJRXAI08p7I5dKOPf4CQO6dmPtzh0snDWHx8cu0MDeEwGBPVMXsW7FKsTwcNq5F8VUz4hTG7aweskSnHVExnqXwsxAn8DIF0z6oS/TfNfkewnWzZs3mdBvONXMSjLv8jm+sXVAIhW4I02nSqtmFCpUKF/lKRQKhk35memjJ/ON0g09qZyzMY/xbvkdXl5e9B3bn+VTl1FWUgopEvxUN+kwqBPW1tavLW/Dmo3snLeXSvKaWEv1uHD1Cjcu3mLhivlIJAWz6j1rhaCWr5xP5cPMFyKF73hzz+8hnbd2Y/GGBTjbOrMu5AzljF2wUSi5lRBMDBkoZUpA48NBPfpQNtmAScUbohZFTpy+zY/+A1n8+4oC8aGruV6u9EJWBpx/Eshq3yXcPrYZH28bnkQksmL1bn5uU5gKrQoT+iKZX+aNpMuPs/Llwx98utHBzonmZSuxKDaGjLg4wjMzsLa1BcDV3JRLjx691YeTRo7AKuwpc6qXQy6RcubxEwZ268rvO3aycPZsnp06SzO3wgiCwP5f57Fh5UrUERG0L1wYM0NDTm7aRKslS3CUSRhRsSxm+vo8iopm4oB+TF+5+r18OLrHaEopKuH74A8qmxRGKofHJnHU7NDwvXw4asZopv80jbJCKXQEBTdVt6nZ4Vu8vLwYMr4P8yf44pBaDgkSnur40XNY+zf6cO2ajaycvQc7SXXkUj22X/Lj6oWbLF25QOtDLR+MKIpnAe0MhJbPjp2dHdev3+b48eM8efKEsmWas3PXyuzXr159TJeOi3n27AUBj+dhZaWZ/O3Xvy6RkYnMnTebZUvfvDx30KAfadSoPkWK2vDdd6UIC4tj0MANNGva543+/ZL5XEtwZYCeIAgZaGb6nwOjgZpZr68FTgEjgWbAFlEU04DHgiAEABWBN4eLAvSk+pjpmGOmY45ujB4bVm1Ex8SQHz3qczv8CSEp8VQrXJH49BRSK2jC81+7dg2zpHi+q1QOACnQxKMo/ucuc+PGDeLj40lMTKRixYpYWFjkqjMiIoIrV65gZGRE5cqVcy0Hc3Fx4W5EEmq1iEQiEJuUxqXAMI7dfU7l9g05vGEli5uXIzk5iVUnHjC9YQn0FQIpKSnYKA0YXac4vyyen+cO18F9ByilcqCkXWFUajXPk+NRq0TiYuKwsLJEJpVyMzIEz0ZV31hGSEgIobdu06/Ky6Ak1VzdeHgzjh3bd3D1yEnGlmqU3RntblSdHnvnsaGjD8ZZTw46K03Zv24ZMzp+h5mBJs3d0ozvPV3ZsGI546fPzNP9/MO8yTPp5VAbY4kuVSyKc/PFY+7Fh+DcsCL9Bg/KV1nZ91SjBsX3beXo4cOkJCYzoc7Q7I5bk+ZNKF+pPMeOHCMzM5Oe3/V942xTYmIiGxZtopOyH1JBs0S4tl5jDl3dweXLl/N19uEbEbXHCfyf8dF9qCvVx1RhjqnCHEWMHutXbERXqU9vxxbciQ4kLDWOSm5VSMhMQlZR80N27do1jKJS+ba45jMrBeq7lubh3RMF5sM7Yck5fRgQzjH/MCq1bcKRLcvx7eJBSkoSK44G8GsnF/R11KSkJGNrps/4Vs5MWDYn7z7cv58qRiaUd3BCpVbzLCERUS0SHxuHhaUVUpmU66HhlKj57RvLCAkJIezOLQbXqpKd9o27Cw9jb7Jzx3Zu/HGcqVVrZvuwr4kJbdetYFOXLtk+7FKmDHtXrWRKqyaYZaW5WZjTvXhhNvj6Mn7GjDzdzz/8On42zfVaoa9rQBn9sjxIvkdwShAeTcrQf8jAfJX1D9W/qY7HkQ0cPXiE5KRkOtbtmu3DZi2aULFyef44fIxMlYp633V/qw9XLNyIp0EvJFk+NNZpwL3Lu7U+1KJFy/8NKpWKoKAgTExMqFu3LqDx34QJ47h27Qkzpu3jz2O3KFHCAQND3ezB5z/UrFWccWPevpipfPnyrFu3ieHDh9Kh7WJkMhldu3XF2dnlY93WR+WTD0BFUQwRBGE2EAykAH+IoviHIAjWoiiGZl0TKgjCP2H77IELrxTxjDec8fRqmG0L3ZfBGDyUnmw8u4aBowezcvJcOjpXoppRCa6FPeKPpECW95gIaMLYFzHI/YTSRFQxok9ParvZY6KQsmHuTBr79KCjT9fsa35fsYJjWzdS1caM2AwVi6alMH3xshyz2YaGhtRp0Z5fDu/A1UTCnzceUsnVACOZyIblcylsqCDo0UMMZFLCY2JxNHIlRaUmKSkRPT09rIz1SY59mOf3+uHt+7ga2JKpzmTp1W1IRYGF/qdo6ewJgYGEK9ScTglnZbs2bywjKCiIwsbKXOlFjIw5d+Y0ZQ3scjwJCU2IoaSpJar0dNB/uXzLVEcB6ek5yvCwtWLj5Qd5vh/QPJWJehpKip4zaRkCMlGGB7Z4WNmz6/a9fJX1b8zNzenYufNrX7O1tcWnu887y7h//z72uGQPPv/BjWKcP3WhYDpcoF1y9n/Cp/KhueJlFNSiRiXZe3YFg8b+gO/PC2lhURMb2zLcin7AWfE2K3sOBzQ+dFXkjlBqmCHw0/e9+MbRGWOpjLWzfqVZj+509Hn5/cj2ofU7fNi8A1MPbMfVBP68FYB3EV2M9GDDytkUM5US9PghRgoJ4S/icFJakpIByUlJ6OnpY6XUIzn+eZ7f60d371JaaU6GSsXM40eQATMuXKR9sSKIgYGESCQci45hRdu2bywjKCiIosrcT0aLmhhx+swZyptZ5PBhSFwcpc3Nc/pQrUapUCD8y4fFra3ZcuNOnu8HND4MfxJOfFwi8RlJSNVynCRuOOu7c/HW2XyV9W/Mzc3p6PP6YHS2trZ07ZE3HxqonbIHn/9grCrCmVPntT7UokXLV8/uPbsZ9uNgMjPTiYtLpH79eixfvgpTU1MWLPyN2rX6UbVqIUqWcqL/wLr077OauLhkTExejjeuXHpE4cJF31lX/fr1qVfPn7i4OPT19VEoFF/lESzwGaLgCoJgimYW3xXNEjIDQRBe3+vPyvKatNcuuBFF0VcUxfKiKJY3lr/sOMWkvcDC0pw69ery48JfOKYXzoyg44SXsWDplrXZs/eFChXibkJijjLVapGDt24zvbYXvSt70s6rOAsaevPnxjUEBGjCyt+5c4dzO7cwt34V2pb1oHfFkoz0KsKEH4dkR1P8hx59+lOy+fdsOn+PYd85075mUSZ0rsaijoX5++4DbPR1sDTUxcnUkKAXyaSkZyAImv9NobGJGJrlftLwJoqW9iAwKYQ/Hp3HRaHP9DJtaGpfib3B95lx7SQHkkJYtnn9G5eaAbi6unI/Lnd0rbtxsRQtUYJIVc6AP8a6eoQmJ+aIOikRJKSo1KSpc74Xt56H417s7Xue1Go18fHxqNVqQBMF83l4OPJ0GaZyJUYKQywUZkS+iCEl7cMOXi8IzM3NidNs1ctBvBiDtX0BLpHQHrz+f8Gn8qHhKz6MTY/Gwkrjw1HLJnHe5hFL4naTVF0P3+1rcvgwIC3nZ1ktqjkWcIUJFavSvVRZWpUoyczqtTj6+7rcPqxXlbZlS9C7YilGli36Zh827cXmi/cZ3tKW9nUKMalnZZb2dObMrUBsjeVYGungbKbP44hUUtIzIGvZ5vPoZAyVluSVwp6e3ImJZs+t67jr6bCkbl3aFy/BjoePmPj3BXZERLF048Z3+vBuTK4THvCPiaeIRwki0nMG4jHR1eN5UnJOH0okpKhUpP7Lh/6hYRR6Dx+GRYYhTZNjIjHDUGaEUmJBdNwLUlK/DB+mC7l/PzKIxU7rQy1atHzlXL16lX59v2fkmHoUKmxOYmIyhw4donLl8qSmptKlcxcMDQyYMasD+voKZFIJrdtUokunJQQFRaFSqdmz+zKzZh5i8OBheapTEASUSiUKheLdF3/BfI5jWOoAj0VRjBRFMQPYBVQBwgVBsAXI+jsi6/pnwKvrexzQLFF7B5of93RVOn8kHKLrQM3TSi8vL+atXMqWw3sZNfHnHOejlS5dmiQzC/b63yM9U0VqRiZLz1+kiLUpbtYvOzoyqYRmhew4emAfAEf37aV5YUekr+xncTJTYqpOz3WkQEpKCvt2bkBPL53l558wbtdNHoTGYWGooKyTITtvPwWgY1lXpp14wN2wBEAkODqeX/68R89BefuAAjRs3JA7inBOPrlIPZsSqEU19vpKfijfmMVNBkJqJmZmZm8tw9bWFpfyXqy/eY2UjHQy1Sr+DHjAI5lAr969CZKlcD8qJPv6FymJREtELoeHZnc2IxMTURsas/jmI0LjEhBFkTuhEay6HYRP79cHyBBFkc3r1tO6dn1+aN6eNnUasGvbdkRRRBSk/BF5E7Wo6YSlqtI5FHGDjEx1nt+bj4WLiwsmhQ24F38rOy06NZL7ujdp1LRhwVVUgMcOCIKwWhCECEEQbr/lmpqCIFwXBMFfEIS/XklXCoKwQxCEe4Ig3BUEwTsrfWvW9dcFQXjySnAILTn5pD7MUKdzOu0A3X/QPL3y8vJi4erFbP9jN6Mnjc3lwww7Y448vkG6KpO0zAzW3DpFYQtzXC1f8aFESkMHJ47uPwBk+bDQ63yY8Xof7t6Arn46S0894+fNd7n/LAFzYwVernpsvxwGQJfKjkze/Qz/Z0kgigRFJDJpZxA9Bgx/961n0aBRIy6nJHDozk2auLujUos4mRgzolYt1vn4IEnPyJMPnb3Ks+bqTZLTM8hUqTl6P4AAZPTq3ZtAMYO74WHZ10cnJxEpwKWwsBw+FI2NWX7nAWHxGh/eDQtnzf1AfPr0eW29oiiyae16WtZsQP8mnWj1bUN2bdX4EImEC6nns32Ypk7j79SzZKrfui34k+Di4oJ9EUPCEv2z0xLTIkk0vE3jpo0KriKtD7Vo0fIZWLxkIV27V2PcmO107FSVhOTV3Lg9A3NzCcWKuWNuruTFi1gsLIzo2KkqM6fv55fpbSnh6UD5smMx1OtOvz6/s3XrzndGwf1/43PsAQ0GKguCoI9myVlt4AqQBHQFZmT9vTfr+n3AJkEQ5qJ5QlAYuPSuSqJUkWyJX0+yLImeP/ekeo137xMSBIG5y5azxteX4UcOIwgCxctVxO7W5VzXSgQhO/KBWq1+7WMJAXLN+E8cO4SGHuE0bOeIqYEOTyPTGLX6PNPaVcZIT4dTwfH8HeyHXCohU6LD8ivhZNxJpnjJ0gycuoBy5cq98z7+QU9Pj+WbV9OoSi3C0+PQUyhQWpliamaa3VnJC+N++YVN69YxcecuMjMyqPxtLRb3749cLmfeqmVMHjGGLTeuIQBG9lZsPXqItcuWcfDscQxkCtL1dJj3++8kxMezcPkSYqPu4lqkGNN9V+Hk5PTaOg/s3ce5tduZWrouCqmMtMwMFi79HWNTJdbmNkjSzJj6YAdKmQGxmclUsa/KU9OoPN/Tx2TeijmM/2kiG66cRS4o0LWSM2/e7Lc+WckPoghiwR68/juwCFj3uhcFQVACS4DvRFEMfmU5KMAC4Igoiq0FQVCg2cOIKIrtXsk/B4gryAb/H/FJfBgjRrIvbQ2piiR6TeyRZx/OW7GU1ct8mXroKBJBgkcNL+z8MnJdKxEExKwBj8aHuT+fr/XhuCE0LPeMhn1sUBrq8DQ8ndG/XWK6T3kM9XU5GZDMmcC7yKUCKnRZfDye9LOPKVaiNP3HL863DxevX0fTGjUISUpCTyFHaWaGqZkZarX69Y+RX8PPUzU+HLtrJxkZGXh/+y2L+ml8OMfXlymjRrP27B0QBAxtbNh6+DBrly1j/6lTGMrlpOnoMG+NxodLli0lJioK16JFmbFi5Vt9eHrlbsYWaYo8y4e+v23A2FSJlbk1CqkRq2KWYygYkUgi5ayrE6d8luf35mOyZOVcRg2bwM0r55AiR2mjYPn8OVofatGi5avnechT4uIS6dajBt17aGKlJCWl8fBhOJOntqZ5i/L4dF7KyhUnGT2mKZcuBuJZfCQ1axXH3sGMpESBo0f/RK1W07FjW/766zRWVhb07jOAvn36fnCwvy+Zz7EH9KIgCDsAPyATuAb4AobANkEQeqLplLXJut4/KzLknazrB7wr4iNAoWKFWHZ4CUqlEqk093mRb0JXV5d+gwbRb5AmmI1araZjk4aExSViY2IIgEqtZm/Ac0YMmgBAvSZNWTxiCOWc7JFINB+WkNh4opDh7OycXfaFCxe49PdBIqzhTqCavvVtcbTUoVV1Aw7eCOFMYAJberVGX0eBSi2iJ5cx9OBlfl27FdusKI35xczMjB6D+xO49yJN3F+Gbv475D4VvqmWpzKkUildunenS/fuuV6ztbVl6fo1xMdrlqX906mYMns2ycnJpKamYmpqmv0lql6jxjvri4+PZ+7EKQyyL0v4sxBMLSwwNDCgR7GK+PquopR3aWxuGNPAtS4pmSkYyg04FHaCRm0b5+l+3pf4+Hg2/r6ec3+ewdTclA69ffD29s51nbGxMfN955KcnExaWhqmpi+P+VGr1ezfu4/9W/agyszku5aNaNWuTY4leu+mYA9UF0XxtCAILm+5pCOwSxTF4KzrIwAEQTAGagDdstLTgRwb27LOaGsLvDmyy3+YT+dDd1b+sfi9fNh/yCD6D3npww4NGxGekIB11jmdKrWaw8+CGTV2JJDlw+H/9mEcUUhz+fDihQOE24n4B6jo19QSR2sdWtXW4+CVME7fT2L3sEYY6MpRqdXoKWQM2OTPzJU7PsiH3QYMwP/kSdoUernc9eSjR1SonrdgRu/y4ZK1vxe4D2eP/4XeJt6EPX2OmYU5BoYGdHKtxvrlayhTtTQ6p634xrYBqaoU9GWGnIk7TNP2H9+H61dv4NTRvzG3MKVr/05v9OGSFfPe6MN9e/axZ+M+VJkqGrVtQOt2rbU+1KJFy1dBhYpV2L5tNZ27vOxPz519kB9/akjvPrUBWLKsB99Um4z/7Wc0aFiamJhkDuy/zowZs+jduzchISFUqlSewUPrMGXaKJ48iWTU8HkEBz9h+rT8Ben8XPz9999MnToBP7+8L+74LFFwRVGcAEz4V3Iamtn/113/C/BLfuoQBAFzc/P3a+ArSCQSxv86l/FDBuJtaYSxXMJfz2Oo3aZj9nk9JUuWpFyTlgzfvYPqNmbEZGRyOSaJqb8tye5o3LhxgznTejOip4LqZQy5cjuRoWsCGd/GESN9mHcpjMETZjFmwxpq2BmjJxU48TSWNr0Hvndn6x+69uzOsCt+LL57nCI65gRlxBFnJmPh8PxFW3wbr5vN1tfXz3WG3rtITk6mT4cuqKLicC9sSqZaReSz52RYWWCmVBL3IpZfly9hgE8/HkQ+xkplyiPJM6zL2dOqbeuCup3XtqtX++6UTXGkq+W3xLyIZ/HQmTwb3JE2HV4ftOR19//Lz5OJ/CuQFjYVkEqknFz+J+dPn2PesoV5b8ynP3i9CCAXBOEUYAQsEEVxHeAGRAJrBEEoDVwFBouimPRK3upAuCiKeY+e9R/ja/PhhLlzGPfDICqamGIklfF3VAR1O7bP6cOm//jQPMuHibl8+OucXgwdKadKJX38/JIYvDCYCZ1tMTSAeWcjGDx2NsM3r6KWmx66cjj2IInWPQd9sA99evZkuJ8fsy5coISxMQFJSUTp6zN/WN63N7yLgvRh7/Y+ZETE42JjkeXDUDKsLTBVmhD3LIY5KxbRt9MAgp8/wCTNijDdxzhWt6Z1u1YFdTuvbZdPq++xfOpBBf22JITEMrnnArqNfUq7Tnn34ZQxU3l86BmVDWojFaScnHmecyfPs2DF/Lw3RutDLVq0fCb69e3PggXzOHnCn6bNNCtybt18St/+dbKvcXOz4trN6ZQpOYaUZFO8ytXm3r1t2NnZATB/wVy6dPVmxEjNpKGLiyV7DwzBo+gohv808p1bQz4358+fp0WLJsyY1QbfVc1xsn/zmdCv8rmOYfmq8PDwYMP+w5w5c4bExERme3vnOnOnV/8BNGregkuXLuFmbMzgatVybBBevngq04dZo07JQC6HkkV16d1BycpjcViZmTNy0kx0dHSp26Id6Wo1lo6OLKpevUA+eAqFgoUrl+Hv78+DBw+o7uyMl5fXF/lof92a31GExyGXy/GLekp5Syds9I0JiYjkaWoCxUqXxMzMjI37NnP58mVCQkLoWKIERYu+O3rYqzx//pyTf55EJpdRp16dd3bOD+zdT9EkK2o4eAGgL9ell15T5ixaTbNWzfO0Gfzp06fcOeXHD64ts9/75k41WOV/iNu337jd6LWI+ZvxtxAE4cor/+0riuKbD5vKjQwoh2ZApAecFwThQla6F/BD1pO8BWjOqxz3St4OwOb8NFbLl42HhwebDh3M9uHc9/DhsuVTmDzNGlVmGnKFQIlSunTvr2Tl1iSNDydMRkdXl9rNOpCuUmPq6MhvEwrOh/N9fbN92OYL96H0eTw6chk3Yp5Q1swVK11jQiMiCcmIo3gZjQ+3HNyY7cMSJTq/lw9PZPmwbh58uG/PAUyeueJppDmORkeqRx15F1bOX0qLNnn34bU/btJO2T37va9lWp+9flu0PtSiRcsXiyiK+Pv7o1QqadmyKQ72Sn5fcxo3dyu6+FTHytqYy5ceUaGCe458qamZrFu3CaUy58kS1/wuM3Z8zRxp1tYmFC3mwP3791+7suRLYtq0SfwyvRVdu717Rc+raAegeURHR4c6deq89Ro7OzuaN2/+2tdiXzzDxd6GsDATAp6FYWEspVxxGWPnP8fIVMntZ0uppNTFyVCXy5FxPLJxplGjggvSIAgCnp6eeHp6FliZBc2h/QfYOG8BLe1dKWvozNK7Z6gc7UYr19LciArhbEwCS2auBzRPYt43hP+mdZvY9tsmyko9UKFi/dzfGTL1R+rUr/vGPNcvXKWUsWuONLlUhpVMSVhY2Bv3br3K3bt3KaKwz9XRLSKz5fbN/HW48jnjHyWKYvn8VZCDZ1llJAFJgiCcBkoDZ4BnoihezLpuB5oOFwCCIMiAlmg6a1r+j/hQH8bEhuDsYkloqAkBweFYmEoo4yVl4rgoTAxNufNoMVVtpbgoFVx4lsRjM/f/pA/Xz/6NxhZFKWVjwurHf1I+tjBNHSpwOyaYS2mRLPtNs03xQ3y4ce0m1s7ZhmtqGdQSFStnrmfkzMHUfYsPr/59DQdZzkGuTCLHKNMiXz50VLnm8qF9uovWh1q0aPkiWbJ0MRnpmSxdOpeQkEjUajVPni7g+fMYxo3dzphR25DLpZw/F4CrmxX165ckKCiK/n3X0blL51yDTwBXt0Jc8wvi229LZKclJaUS8PB5nlz6MTADHOMAACAASURBVIiPj+fUqVPo6upSs2bNt04qXr9+gwWLGuS7js8RBfc/iUxhSFxCOsmJcThY6WFkICcqFip6mGJjEENDKwO6VyhB7eLujKrhhV1sKHt27fzczc6FWq0mODiY6OjoAi03Pj6e1bPnMKNGXWo6uNDItSi+tZty7cVTFj+4yM6oAH77H3tnHVVV9jbg59yguztFDOzC7u7u7hpz7K6xu8buscaxsLEDC1sxUESkpPvC5Z7vDxyUARFGnPH3zX3WYrnYnB3ngM89+5y933f3ti8mPM8rISEh7F+1m8HW3ahqVYEaVp4MtOzCsmlLSEpK+mI9OxcH3ieFZykTRZGI1Ng8v5WxtrYmTJU9JUGoGIOtfY6pHHNGpECjPuaBI0B1QRBkH4PlVAKeiaIYCrwTBOHPO9G6ZOxN/JN6gJ8oij9GNBQ1PwxymR4xMWkkJcZhZ62Fnr6cyAiRCqWNsDSNoqWzjAE13GhQ0pFpTYrhoHj7n/PhpoXLmFmhGdWtXGloX4xVVTrwMD6Aze8vcSzhMWv2bi0QH25fso9mmgMpaVSF0gbVaSIdwIJJy3P1oWMhOyLSQrKUiaJIPFH58mGULHvguGh5hNqHatSo+eE4fvw4SxbPw93dilNnx6BSpVOsuC0mJnp4eNjzx5HRxCdt4ffDo7C3t2fKRC8MdPtTqfwsypVrwNIlK3Js96fho1iy6BQnTtzPyO0cFku/Plto1Kghtrb5cGEBsXPXDpyc7Fm1egYzZo7Cycmea9euffF4FxdnfO++yXc/6gnoP0T7zkP4ed4j4hKSiE1Q8CwgmZnrounZogitKmsSmpA1317zos6c/5jm5Ufhps9N2tVrzuweoxnasgfDeg8kJib7hOrv4OPjg6epBdaWFsSmKUhLT0dHQ4MWLu7IdbVp37MbTk5O39zPpQuXKC0thlTyKRCLtkyLwhJn7ty588V67Tp34HLKY4ITPgCgVKVz4v11Ktavgp6eXp769vDwQGEp43bY04x0MqLIk8jXBOnEUrVq1Xydhyjm/etrCILwG3ADcBcEIUgQhL6CIAwSBGFQRl/iM+AU8JCMiKubRFH88xXFcGC3IAgPgdLAvM+a7oR6uZmaHOjQfgiTxz0mPjaR2LhU/J6n8MucWHq0L0yLehqExGcNEtq6jB0XTx75Qmv/Djd9fOjYqAkLBgxjVPvO/NS3f4H6sIKBDVYWlsQpU0hTpaMt06CpvQcaOlp06FUwPrx44RJOitJIhU8+1JRqYZ1SOFcfdujante6PkQqMlLOpIvp3E08S/UmlfLlQ6mjisdxDzJ9+Crej2izcLUP1ahR88OxZs1yZs1phSARqFJpOhaWBrx6GUpsbNaHddeuPqdylWrcu/eYiIhIwsMj+GXeAuRyeY7tlilThh079jBp/HFMjQZRtPB4TE09aNeuE507t6NlyyZs2LgBhUKRY/2C5Pnz54weNYJLVydz6swYrlybxMYtPWnTpuUXH0qOHTuRsaP3cePGy2yR7nNDvQT3HyI68gOoZMxbF4GOlgAIaEikxMSloUgTkUuyPgtQKJU/VJLZsLAwfhk9lZ+cm2KomXGD8eDDKyYMG8P6XZu/uX2ZTEaqqEIul2Pj6EBYcAjpKWkEJ8VhULYEoydN+HojeelHLiMNZbbyNJRflAOAhYUFCzYvZ+HUeUT6h5EuEWnQujFDRuVtszVkLPtbsXkNC2f+wtxru5EA7mWKs2bWhnxFJgXIc86IvDQlip3zcMwiYFEO5feBHJeziaLY65sHp+b/JdFRHxASZSyeFYG2jgRBBZqilJiYNBSp2X2YkpqOTP5j+XDR+ElMKVsdI+2MwDp3gwKZNGIUa7dv/eb2P/ehraM9YcEhKBVKQlJiMfYszJiPEYe/FblcRjrZ0+qkC2lf9eGq3YuYM2EB1wI+gExFs14NGT5maJ77FgSB1dtWMX/aArZeXoWAhGLli/Dr3HVqH6pRo+aHIioqisePH+FWuBYP70XSf2AdVi4/RdPmZejSaTUrVvXA0dGMgwdusXTxSa5fv4UgCOjq6uap/YYNG9KgwVOio6PR1dVl9pyZTJw4glFjGmBgYM3GDWs4eGAvXl6nc3Xzt7Jz1w569KpGsWKf3rw2alSK0mUc8fLyon379tnqNG/enNi4hfTsNoXw8LynQ1RPQP8hTh/fw8YpJQh9/wYLIxEdLSnhUWlMXRdAaAQ0cfz01FgURfY+ekWTQaO/2J6vry/bVq8i7H0w9i4u9BsxgiJFinzx+G/l6O+HqaVfNHPyCVDKvBAXXz0lODg4M5rX36VKlSqsnj2HZklJGOvo4OTqTExyMgFhAbSvUZOerTuTnJBEuWqVGDRy6N8ORlK3Xl22LdpMpbQy6Mi1AYhOieGt9D3ly+e+LahIkSJsObAjI8+hIPytoCUGBgbMWfJL5hN/ieTvLEIQ/umoj2rUFCinju9hw4wShAa/xsJUhba2jPBIJdPXBBIaAW0KffrQFkWRXbcDadLry5MuX19ftq9bQXjIe+ycXOk7bNR39eGxQ3/QxMopc/IJUM7OgZO3LxWYD1cmzqNhciLG2ro4uroQl5JEUIIfHWvWonuLriQlJFGhRkUGjxryTT5cP28bxdIroiXNOJe4tGgi9d7myYe7Dm/9Zh/OWz5X7UM1atT8sCiVSurXr42xiRb79/lgY1WHwUPqceTwHe7eeUOFCi5UqTSdmJgkDAx0WL9+E8WLF/96w39BEARMTEwIDAxk3dq1PH0+HzOzjHRnbdtVpEa1eRw+fDjHSWBOiKLI1atXuXXrFra2trRq1QotLa1c68THx2Ntk30Vi5mZXmZ6sZzo1rU7Xbt0IzY2NkuqrdxQL8H9h1ClK9DSlGJj50BYDIREpJGuErn3IpGug+fyQNBjziVftt99yqhTPlhUrk39Bg1ybOumjw/Lxo6hm7EhK2pUoYWGlOmDB/P8+fPvNv7oD5EYa2T/ozSS6RbIsjMtLS0mL1rIDN/rbH54ly0P7zL9zjVcS5fh2voj9NGrzniHVhjfimdgp1657k/KDWNjY35eMJ5fI/dwPOwcf4SdYmfiYeat+/LyiL8ikUi+OWKmIAh/82YLEEFUSfL8pUbNj0amD20dCYsUCA1PQ5Wu4t7jRLr1mcftZFOmHn/K5quvGLLvAaZl6lO//hd8eNOHVVN+YpA7bGlfjC62icwe2e+7+jAmMhIT7ewpVYw1tQrMh1OXzGe+3wV2PPdhl58Pc59641amNOeWn6RNemMGGnRH5p1Ov/Z9vsmHU5b+zEk2cCPJi6vJh7mktZvFG+epfahGjRo1ZOz91NRScujwSHbvvArAmFG7KFbMln79a3PPNwARMDTUZuLEacTExtCoUV2aN2/Erl27UKlUQEb6qtTU1Fx6yuDSpUvUq18ic/IJIJVK6NylIue8z+RpzAqFghYtmtC/f1cCg7zZsnUR7u6F8PPzy7Veo4aN2bPrJgrFp5Ux4eGxnDr54KuBBwVByDHI0pf4z70BjY2N5fjho4QEBlGmckVq1a6V/+U+uaBQKDhz+hSv/B7g6FqURo2boqOjg51TMR4+D6CkuyHOLm4kJSVx8nIITq6liY2OZuKceSQnJxMWFkbXokWxsLD4Yh8bly1lbMVymYngC1uYM6RkMTavWsnC1WsK7Fw+p0rdGuw/vwYPcxdEUUVcXBzR8bHcD3v5zU/7/6Rc+fLsPXUSX19fVCoVI4oVo1eTjkwq3B6JkHHjUMG6CBGBcRw/cowOnTv+rX5q1q5FpYue+Pr6IpPJKFu2LDKZjKCgII4dOsrLFy+QSQQKuRemWeuWBXZ+BUk+0w6oUZMjsbGxHDt8lOCAYMpVLf99fHjqFK+ePcTRrUimD+2divLQL4CSRQxxdnYjKSmZUxeDcXIpQ2xUNBNm/pLpw/Zf8eHmFYuZ2tAda6OMt6ZFbEwYUwO2rlnO/JXrCuxcPsezVk2OXltMaRt7RJVIXFwcMXFx3HkXwOQC9OH+M16ZPhxTrBjdGnRhqHXvTB+WNvMgKjzmm3xYq3ZNPK9VytGHRw4e4+XLl0gFCW7uhWjRtoXah2rUqPlP8fDhQ2rVdsPZ2YKrN2Zw8riEI4fv8jZoJUZGuowclREB9sD+m4wdvQT3ItYMGVqL1NR0li6ZzcGD+4iOjuTmzbtIpVLat2/DsmWrvvim0MTEhPfvo7OVBwVFY2JSLMc6oihy5coVXr9+TalSpTjnfQ5lejj3H81GLs+Y6q1be46+fXty7drNHNuAjKXA27aVoWa1efTtX53ExFTWrPJmxMhRODo65vfS5cp/6nHgy5cv6dm8A+93X8fyTjyn521lYLc+BbaxNyYmhv7dWvH+4nIqa90k5uY6+ndtQXh4OMNHTWfhLgWHvcN5/iaerYcCWLIliB6lJThEnmT6sE68fvWCmjVr5nqzBRAfGZU5+fwTdwsL3r56VSDnkRNVqlRBXsyCHS/PcunhLW6+eMi6R144SKzo3bobISEhX28kD2hoaODp6UmVKlUIDw/HTsss82brT9z0bfB78OSb+tHS0qJKlSpUrFgRmUzG6ZOnGNq6HyeW7efdsduYXgnlw64rDG/XE++z576pr++CSsj7lxo1OfDy5Uu6Ne7M8/UP0PJW8ceUvfTv0q9AfdivS2venV5DJdU9oi5tol/nlhk+HDmDRdtSOXIunOevE9h2IIClG4PpWVKKQ+SpfPkwISYic/L5J0VtTXj7+mWBnEdOVKlSBdHVnvW+17nge4frT5+w6PoFCmmZ0a99l+/mQ2u5RTYfumg58PTe0y+0kDf+6sNTJ07Ru/Fg9s45woPtT0ncnc79Bc/o13QA586ofahGjZr/f6hUKqKiolAqs8YJKVSoELdvBQLg5GSOk5MZOjoaGP3lc+fDh1h0dCScODWaVq0r0KGjJ7v2DODs2bN07+lBTPwG3gQuRVMrjDZtWuQYsCcxMZGnz57y+FEAu3ZezTzm3r0Atm25Qq+evbPVUSqVeHqWZ/DgHnif307Llo1YvmwhY8c1ypx8AvQfUJvnz5/z/v37L14DiUTCnj37mTDhF65fTcbvmQZbt/7GtKnT834h88h/agI6f9JM+lrVpL5DWTzMnensUhP7DzIOHThYIO1vXr+STqXT6d/QgQruZvSsa8+gGhqsW7EABwcHVm88SqxuNzaesuHURRUnpzamSQUH6pexYVVPN7avnZ+npVTahgZEJCZmKXsTGYmN/ffLFyQIAv1+GkSoeTrekc8IlqbRr3wXBpXoSGONCiybu6TA+7S2tuZ9SmS2/6QBiWG4FnUrsH5SUlJYPXs5jfQrYyAIjCzcmOrmHpTQtGG4cx1Wzl6Qp2UTCQkJ+Pr6EhT0naPs5yflgPrNgJovMHfCbNrpNKW6hSdFjN1oYdkQswA9ft9fMOlONq9fRcciEvrVcqF8IQt6VHNmYAU91i1fiIODA2s2HCVOozubjtly2lvk5OQmGT4sbcOqHoXZviZvPtTSN+JDXHKWMv+wGKztCvZp7ecIgkCfoUN4oyPn2Lu3BKYLDK/ehjGezehgUpQVv2SLT/PNWFtbE5r2IZsP3ymCKVS8UIH1k5KSwtJpqyiXXgftVE3a63WnpFYFHNMK0UrekSVTl6p9qEaNmv9XbNm6GWdne1xcHLG2tmD2nFncu3ePFStWoFAoeO0fyexZh0lISEFLWwOVSuTuX1KP7P3Nh249qiGTfVpF9NueG3TpWoXefWoil8swNdVn9doeBAa+5u7du1nqJyUlUbt2da5ePcjU6S2ZNHEfrs6jqFB2Go0bLGHN2l9xd8+afxng3btAPKtY8uDxHLbt6McL/4WIojLLOAAkEgGpVEJ6enqu10IqldK2bVt27NjDxg2bqVmzZn4vZ574z0xAFQoFMcEfsDMwz1JezbIYF70K5onuXZ+L1CtjmaWsclEznj24BWS8Vu/dZxDFS3gytKEz+jqfojpqa8qo5KTNw4cPv9pP72HDWXbrLhEJCQAEx8ay5v4jeg/Pe0TW/PD27Vs6N23DyiHTUNx7T0paMsXMC2Gnn3GuRU1ceOb7qMD71dfXp2qTWhx4c5kUpSIjbUnEG24pA2jeumWB9fPkyRNcpDa8iPanqmmhzP1MOhIt0pNTcdU0++q6+a0bttOuRidW9tvETy3GMqj7EBL/8pCgoBAp2LQDav57KBQKot9FYq2b1VfljUtz/rh3gfRx99pF6pbIulzT082CZ/dvA5986FHCk6ENsvuwonPefNhz8AjmnfEjPC5jshoUlcDiC6/pNWREgZzHX3n79i1dW7Ri/agJCH6vSVGkUNLKCUejjM+WEpYOPL//9XHnF319fao3r8nxsDOZPnwe84pHGn60KGAfWqY58jbhJSXkpTICDAGaojZpiUqs0m2/6sPNG7bTuHpHpvTeSI9mY+jbTe1DNWrU/Dj4+/tz69YtUlJS+P3335k7Zxr7Dg4kMmY9l65OZMOvK2ncuC7PX5zg90PriI9P4MrlcCzNhvDwQSAuLoVp03IFWzZf5Pr1F0yauJ9HD4N5GxCVpZ/X/mF4Vs76gFAikVC2nDOv/rJqcdv2bZhbSNm+sz/x8QosLQzQ0dbg6dN33Llzjw7tO2Q7j5SUFGJiYpg+o3XmvatcLqNVm/IsnH88c/8pwJ7d17G3d/jm/NEFxX9mD6hUKiVNTEcUxSwBE+JTk9E31c+lZt7R1NImMUWJge6nGylFmgrpX9IH6BkYEvtG9dfqxCSr8hSyuWbt2gAsW7uG+OgYjC0sGLVwESVLlvzGM8iOSqXi54E/0dWgIg42FrwRX6OhlLHi6QkcPW0w0NRDqUpHKv8+f0qjJ41jl80OVuw5RJoilSJlPFg9YSMGBgYF1oeuri5JYgpmUj0SlJ/epKhEFRpSKQnKlFxz2/n4+HBs1Sk6GwxG8jGf3uOHd5k9aS7zV8z7Yr1vQr2UTM03IJVKUZLdhwlpiRgYFJAPtT/6UOdzH6Yj/UtwGz0DQ2JfZ/dhbF59WKs2MIvZv64iMeYVRuaWDJ+z/Lv5cPzgYQxyKIqTsRlv9F+jKUqY5+uNs4klRlq6pKnSkXwnH46dPJad1jvZvns/qYpUipUtzrpJvxa4DxVCMjoSY5JUn95Ai6iQSjVIJvmrPty54iRldQZm+vDdPV+mT5rLYrUP1ahR8y8SEhJC164defbsKdbWxrx7F4mZmQlLV3SmQgVXAO7eeYOFhT4XLk9BVzcjauzBAzeZOvk4MTGx3Lhxg9GjR3PhwgVWrlzC/Hln0NPXo0WL5vxxyIsOnSpSr54HoiiiEkW8jt+nV+9PbxEVijSuXvFj1sxSWcZ24fwZ2ncoR6MGC3B0NGP5qp4kJ6cya/ohevXqzoULl7Odz5/LhXV0ss4zBg2uR41qs6lSaQ5Nmnng9yycSxf9OHnyzDcHjSso/jMTUJlMRvmalbly/zE1bEsAoFSlcyT4NgNGf3uOyffv3xMXn860TdcZ3sgGQ2NjzMzN2eYdRP1mWdOKNWjYmCE71lC3hAITfU0A7j57h9c1P16EDMHc1oGBoybi4eHxxf5q1q6dORH9njx+/BjLVB0cDDL2YRmbmhAXFk01Y3duhz6mrqMnZ4Nv0KhD0+/Sv0QioUefXvTo0ytf9ZKSkli1aAVXz1wBUaRCLU9GThiV442au7s7CYZplJBbcfzFCcoYO6Et1SBRTCFGJZJipIGzs/MX+9q3+QCe8rqZN1sAxfXKsvvKalJTU79LPld10A0134JMJqNC7YrcvOqLp1k5ANJV6ZyLvcLwPl9O/5RX3r9/T3yikhk7LzG0tgOGxiaYmZuz/epb6jfPGkL+rz4UAV+/QLyu+/EifBDm1o4MHDkpdx/Wqv1xIvp9efz4MfbIcTI2A8DI1ISE8Ejq2DhxI9CPxoXLcez1fRq0bvFd+pdIJPTs15Oe/Xrmq15SUhLLF6zi4skriCJUqVuRMZNGftGHKstkzBXFuRx5FDdVETQELVIkSSjlyQhWYq4+3Ln5AI6S2ll8aKdThpuX16p9qEaNmn8UlUqFt7c3d+7cwcHBgfXrV1O9phUnTi9BLpfx5EkQlStNp2xZp8w6+/f5MHpsk8zJJ2SkQZk+9QhPnz7NnMBVrlyZmTNjsLM3pFPn8ty985bUtFTatFyKmZk+IgJymS4KRQrTph6kX/9aREUlMm3KIWrUqEnRokWzjNXE1JyzZx4jk0nY/dvQzH6qV3fHvdA4fH19KVu2bJY6enp66OrqsmP7Ffr2+/QZuH3bVdq160DLFq3xuelDrZr1+HV953xFqf3e/GcmoABjp05k6ujxLH5yBAsNQ96kfKBt3y5Urlz5m9qNi4tjVJ/eDCrswO3ANKbt9sfGNIzn0c+p1rgTg3r1y3K8sbExI6cv4ac5E3A3lxASHslT/1A29a9NMXsT3oTHMXvcIOas2ZnrB/0/QVJSEjrCpzcWRsZGpCoUpEancifiPo9UAXjUKEO/wQP+xVFmRRRFRvQdhmOQOSMsuwPge+0xQ3sMZvuhndnC/QuCwJKNK/h50GhkJgb8/Pg3HHVN0DU3Qm5iwoK1y3N9YpSYkIiWVDtbmzLkKJXKgr/hElHvZVLzzYybPoHJIyey8cFuTKXGBKWH0GFwpwLx4eh+vRjqYc3N1ynMPBKAtVEQz2OhetOODOn9FR+GRfI0IJRtIytTzNGYN6HxzJg4gNkrd/8YPpR++tg0MjYiTaFAEZrGtSA/biaGUrRKefoO+rF8OKTXT2g9cqCZ7jAAXhy5x4AnQ9lzZHuOPly5dSkj+41FO02HX8OWYSm1xMDaAAMbXZZuWJy7D+MT0cjBhxI01D5Uo0bNP0ZSUhLNmzcmIiKI+g2L4XViF37PAvG+OASpNMN7xYvb4epiwbmzj+jRswYASqUKDY2s0yNBENDQkGUJULRl6xY0tZI4fuJnQkJimDHtd5av6E7HTp54n3vML/OOkZgg5/ffD7NixRIqV5yDnp4u3Xv0YuKESdnG27dPfxo2rMv4ic2yOFZTU07jxqXx8fHJnID+/vvvLF78C/7+b5g1aw5TJ//OTZ8Aypaz59xZPx4/CuXy5etYW1vTvHnzAr+2BcF/agKqo6PDkvWrCA0NJTw8nEKFCqGjkzWXW0pKClt+/ZXLp04hCAK1mzalV//+uX5oHj38B/UtjSltZ0NpOxtiksry+/2HXI64z71bl5g7Yzz9h4zB2to6s06lSp7sPuyNn58f44f15fiEFmhrZkz0nC0MGFzFkpWL50F6OqHv3mJmaU3PISMoX75Cvs45KiqKX1esxPf6DbS0dWjRpRPtOnbI8yv4UqVKMS85hPehIaTEJSGKInr6uvjrxjN82gQqV66Mqalpntq6cuky21ZvJPpDJC5F3Rg89idcXV3zdT4BAQGsWbgav4fPMTI1ovdPvalTt06WY54+fUr62xSqWJdFFEViY2OxSjLhdvB9jhw5QuvWrbO1a2Njw64jv/HmzRsSPu6t1dfX/+INryiK/Lp2PdtXbSE2MoYbadfp5zYOE82MNyPhKSEY2xtm+/sqKET1kjM134iOjg7LNqz4ug/X/8qlk6cRJAJ1mjal14B+ufrw2OE/aGSrTxkHa8o4WBOTWJKDd59w5e0j7t2++HUfDu/DqTn1P/nQSp+hdUxYtWQuqJSEvn+LqbkNPQeN/Fs+3Lh6Ofd8rqGlrUOzDl1p26Fjvny4IPYD70NDUcQnIIoiunp6PJWmMeiXqf+KD1fNX8OzB88xMjOi38heOfow7rmS8nqemT7UjbXkSdSdXH24z2t3vny4fs2vbFy2jejIGMT0G9RzGY3+Rx/GpoRi6WSg9qEaNWr+MRYtXoiRcSonz8xAKpVw6dIzxo3Zkzn5/JPOXasw8qddiCLUq++Bs7MZixd60bxF2cwost7ej4mJSaFQoUJcvXqVGzducPLEUfr2q45EImHTxgu071CJ3n1qcuHCUwb020zJUvbo6UHNmtVYuGgxW7fuzHGcISEhLFj4CxfOn0NTU4v79wKyHfPML4SmTW0B2LZ9K7NmTmb5qi6UK9cbn+sZW1XkMlcePkinXr1u7NjeA339gtlO8734T01A/8TKygorK6ts5aIoMnrQIEqlprDQszwicOzKJcY/esiy9b9+sb13L19S2eTTa+1zL/yIFfzZOswOl8KW3PN/yJjBnVm77XCW199SqRQ3NzdMdeWZN1t/4mSizfXtx9nYpwGFKpYgMCKOBVNHMWjGYipV8szTeSYnJzOkW0+aG9rSoWwdElNT2bt9H8GB7xgxbmye2tDW1kbf2oy5V/bSyqoimhI5p/3PE2sCjRs3znPOwNMnTrFz9hq6OtbCzNkQ/5D3jOkxhJW/bcTBIW/Re4ODgxnWaSj1JPWopl+N2LhY1o9dT/yUeFq2/RSE4+3bt1hjjiiKvH3zFlmqgLZUC3uFFfPGzsbY0IhadbIv1xMEARcXlzyNZfTQkbw8dJ+Bpi3Rtdbhbsxjlj6eQAfngaRIk3il+4iVi5flqa38I6gTqqspMHLz4aiBg/FIVDK7dFVEEU54X83w4a/rv9he4KsXVDczzPz+9LOXRCnfsnWoPS5ultzzf5SrD830Zdl86GyhyfUNx9g6qhpuTZ15G57AvNnDGTBpeb58OLx3N7oU0mNwi5IkKFLZcngLq4MCGT5mXJ7a0NbWRs/SkknnjtHJ1QMtmZxjD24Sriv/ez50qIWZkxH+Ie8Z3WMIq/Lpw0Hth1MppTFtdOoSFxzDip82EjcrnlZ/8aGRwhpRLhLw+i0kS9EQtDFW2jN9xDyMDI2pXadWtvbz48ORg0bhs/05NSW90EaPl6m+HH0yjSoufVFJE4nTf8LGJWof/pcRBEEqimLu4TfVqClADh7Yy4bNXTInnGXKOOLvH0ZAwAecnD4FJH38KJhOnbtxYJ8/E8cfwszMBC0tXSqWm0nbqB9xOQAAIABJREFU9mUJfBvNkcO+dO/eE1dXZxYs+IVJk0YQERFHsxZ2APi/CqN+gxIkJKTQucMqfts/nPR0FRvWe+NexJJx48ZSxL1otoiyHz58oEqVSrRo5cGGzZ15+jiIYcO2c/DATdq2q4hSmc7aNecIfh9PkyZNUKlUzJwxjd17B1KpUkZwI1MTbZYs68TO7c84e/biP3NxCwC1tT/jwYMH6ERG0LJ4UTRkMqQiNHR0IOHFi1yj/hUuUZJHHyIBSFWmc+7VI6a0s0VbS0BTU4MqHua0LS9w6OBvAKSnp3Pr1i3Onz9PUlISSqkWcUlZw9pfuv+SGoVtKGSZkajWwcyAKfWKsHll7ulOgoODOXfuHE+ePOHE8eOU0zCgiqMrEkGCvqYW/Up6cs3rJHFxcXm6JsHBwSiDkxjnOYQYTTlvUdDStQklNAtx8cLFPLUBsGn5Wvq5NsRcxwhBEChkYkdL43JsXbvxq3VVKhW3b99m5qSZVFJ64mLggiAIGGka0d6sHZuXb86SmsDd3Z23qmDiYuOQpgoYyg3QkGjwPj2M7nYtWT5zSZbIYPklPj6eM3tP0NeqLaaaJmjJtKhqVp5GZpW5Z3qJKuNLse/MHtzcCi5VTBZE1Hnv1Hx3Hjx4gFZ4FE0Le6AhzfBhPVsXYp+9+ooPS/EwNCOJdqoynbPPnn70oQRNTc2PPpR82YcS7Ww+vHj3NbU8zHGzzZjYOlroMb2tE1vW5J7uJIsPvY5RzVxKTXc7JBIBA21NRtQqzo3Tx/PlQ8LjmFWvF1ESbV4rVHQoXocKBvZcvHAhT23ARx+6NMJcxzjDh8Z2tDIqny8fzpg4E4/4qjjouiIIAoZyYxrqdGLD0i3ZfBihEUhsbBxishRdqSFyiQaRsvfU0enMwinLvtmHR3edoYGsJwZSM+RSLYppV6GcpC6xFpfpNLkMR8+pffhPIghCnCAI8R//jfvs+2RBEP7+L/vbeCUIwiJBEIr9S/2r+X9KWloa169f5+7du1ncp1Kp+Hxxi4GBDpOmtKRq5Rls3XKJs2cf0afXRnzvhjBv7jyKFvMgNVWBiakmb9++xdDQivhYR4oWbcyOHbvZv383PrenUbiwFfcfzaZDxwrMnvUH4eGx6OppcurUQ0543ad8BRf8X4UxoO9GGjYqybz5nejUqRJt27UiNDQ0y9jXrltD3XqFWbqsK+XLu9CjVw327h/OgH6bsLcZiZ31CA4fesXp0+eQy+XEx8cTERGVOfn8k7r1PHj48PF3vc4FzX/yDeiXePXqFUX1dUGED+HhJMTEoCOT4aBMY2ifPuw9cgRzc/Ns9Zo0b06/XTuxePmawmam2BhLiUxJRt/YOHN/TWlXA7bcfcCbN2+YNHwgxQ2lGGpK2fBLDO4VqzL96DVG1XPG3lSPO/7hrDj/lt+GZF23bW2sR0K0f45jF0WRRbNn8/jSZUobm3IiOYm7we8ZWqxSluMEQcBZ35igoCCKFfv650BAQACOcivMtI3xNCpFZFgkGjGamCToMm7wOA6eOfjVfVkqlQplggI9u6z7ggqbOOD95HyudQMDAxnZeyTGcabc9/fFXteeKCEKE1MTADSlmkgUElJSUtDWzmjf1dUVq7L2HDl1hro6lVCoUrkee480DSXFTd25E/aM4OBg7Ozsvnr+OeHr64u9hhUySdb/PsV0XHmdcovO3Tr9rXbzgzqdgJrvzatXr3DXMcjmQ1tFOkP79GXvkcM5+7BZc/rv2oHFszcUsTTF2kRKZHIy+kaf+dDFgC2+H33400A8jAUMNaVsXBBH4fJVmbL3MmOb2WFvrsvtFxEsO/Geg5OzLi21MdUhIeZVtv4hw4eL587C78Z5KtjqcS42lVuvw5lQq0iW4wRBoLCZXr586KpjhoWeIbWtixAZFoF2sgzrZDmTfxqLi9cfefehbU4+zD0FTmBgIMN7jkI/ypwHb3ypKxYiShmFidmfPtRCSJZm86FLZSuu7D+GB7VIExU8U/kg6qTholecgKR73+xDM9EWyV986CAtgl9iIF3UPvzHEUUxS3QpQRD0gKHAAODQvzIoKAl0AjYJgiABtgB7RVHM29MfNWpywMvLi/79+2BlZUBiogKJRJO9ew9SqlQp2rTtwLIlp9m1Z1DmZ4+OjhbmZtacOhFGRMRjypbzpG7dolSuXAE9fXjhvwgTEz1SUlLp33cL8QlxLB6zhIGD+jN8RD0KFbIiJCjjs2Pp8m78fvAOhZxHo6MjRxAEEhNT0NXTZMqk/Vy4PIWiRTOWzdauXQxBEFi2fCkL5i/MHP9Nn2sMGJw1Gm6TJqUpUdKFIYMnUbNmTWxtbTN/pqenh46ONs+fB+Pu/inN2d27b3BxcfqOV7rgUb8B/QwXFxdeJiaRkJhISlwsdvr6mGhrE5aSQgd7e+ZMnJhjPW1tbVZv30FwIQ9+ue/HnfeJ6BibY25ukXnM44B4nAoVZ9ro4UyqYsfIWkXpXbkwa9uUI+jeDep0HcHKeyr6/vYK7yRnSlaqQZIiDVEUSVVmpEsIi01Ex9AkxzGc9PIi5uZt5lWrRUePkoyp4El350Ls9L2W5ThRFAlIiMnyB50bjo6OBKaFoVAoiAqNwkRijr7UkA9pCXhKajN2wLhsidH/ikQiQaqjQVJaSpbyV9FBuBbJPYH6uMHjqZvSgGbGLShuXIKk9CSiw6JJScloS5GuQKmhRFNTM0u9+SsXklpKxrqwvWyM2I/cRIOBpbsjCALxyoRvSlvg6urK+9RwVGLWB8mvk99j5ZS36/otZOS9E/L8pUbN38HFxQX/5IQMH8bGYaunj4mWDhGpybS3zd2Hq7btJMixDLN8XnHnXRLaRuaYW+TgwzHDmFrDmtF13elbrRDrO5Qi6P51ancczdJrcnpuDOJMhDslK9YkMUWZ4cO0jz6MTkbHIOf9lidPeJH05Aor25Smh6cb0xoWZ2A5K369cC/LcaIo8ioqIV8+fJ0UkZFHNSwSKy1jjDT0CFck0cS0AhOHjPn7Poz5ug9/HjSBilFNqK3dGnf9UqSISUR95sNUlQKVZlo2Hy5eswCjGkpOSTZwRroFHQsNWjv1RxAEkon/Zh9GEoz4Fx+Gim+xc7X5Qq2CQ+3DLyMIgrEgCDOAR4A+UEEUxZ//jbGIohgviuJGURSrAOOA6UCIIAjbBUHI/Q9fzX+axMREOnVqR8WKZRg4qD8vX74EMrYX9OzZjb0HBnHbdwZP/OYxaUp9mjZthEKhYPy4CQS/V1G54mymTT1I65Yr+WXuSfbvP8TBg0fYsmUXe3/bjVzzHSJJLFvRDROTjBRTWloaLFnWmd/2/IZCoSA2JgorK8Ms49q29TLR0XGAyMUr07hxaxYachlex3yxtDLMnHz+ScfOlblyOeOly9OnT/njjz/Q1zfkuV9IluPS0pS89g+jfPny2T6bpFIpI0eNplePTTx/Hvzx+igYOXwPY8fm/Jn8o6KegH5GmTJliNIz4PC9+xjINUhLT+fYK39SkNC8eHEiPgvI8FeMjY0ZNWEC+0+dpt+IKaw/G0NcUsYE0vdFFPt8lJSrWBVLqQJn80/7njRkUtoUtSDs/TtWbd7N7qNnmT5vMT+Nn8LYQ7fpuvoIw7ecoOvqIwzdfZXeQ0fm2L/XgQO0LVIsSzCNBsU9CI6P4XZQAKIokpyWys4ntylfrzaGhoY5tvNXbG1tcargxu8vziBTaSKKIvfjn/EyNRRPy1rIo7R58+bNV9vpObw/W/zPEpMSD0BgbBiHI2/Tc3C/L9YJCgqCD2Cjm/EfsLptTS6lXCJWGUdMdCzxqfH8EfEHPYb2yBbJUSaTMW/ZfMydLBlevi+NXeogl8i4+eE+bhWKfNMNl52dHVZuduwNPUlKekZC+GcJrzkVf51Js7JHNitwxHwsN/uPLDlTU/CUKVOGGCNdjj68g75cjlKl4sTbF6RIRZoW9eDD69x9OHL8RPafPEu/nz76MPEzH95Mo1zFqljJFDhbfHKRhkxKOw9zwoPfsWrjHnYfPsf0uUsYPnYqI7c+puO80wxe5U3HeacZsO4uvQblnDLm1O/76FbeKYsPm5QrzLvoeK77ByOKIkmpaay/+owyNerny4d2ZYqy68ElNAQ5ogg3I17zLCGC+s6V0E+S5c2Hw/qz5fWZTz6MC+OPiFtf9WFaiICVdsabykoWtbiNNwmqDB8mKOM5m3iA3sNz9uGCFfOwcbWgo+swKls0RCaR8yzhNh5VCn+zD+2L23BZeZBUMQVRFHmnfM496TmmzP4HbojUPsyGIAgWgiAsAHwBJVBSFMUpoihG/YtjkgqC0EIQhD+AFcASwAU4Bpz4t8al5sfmyNEj+Pu/oloNPZauaImVdTTVqlXmyZMn7Ni5g85dPKlatTCQ8VayS9eqFHa3xMvLCz09PS5evMrMmcuQS0vRutUg/PxeZqY/mTtvNv0GVGPxks4kJaZib5/1gaaZmX7GvXNyMvXrN2HXTh9UKhWJSQrq1p7Lz2N3s2XbQAoXtqZIERucnMzZf3AEo8c0JSDgAykpWbeSvPYPw8zMgtatm1O/fk22bluAt/c55s87yrVrLwBISlLw85i9lChRksKFC+d4TcaPm0CbNr2oXWMBRvoDeP06gmnT59K2bduCvvzfFfUS3M8QBIHlGzfSpn59vC9dQVMmp4KTE5MbNUIQBKQSSZ72yvTpP5RDB00ZuX07qYpEnNxKsGD1RJKTk5FJss/5ZVIJ6cq0LGXx8XHYGOkwrLwDWlKRVJXA5vuRxERH59hnulKJXJI1AIZUKsXe0YFHVjrsu30WDU1NmrRvS9de+cshN3vJPLq16cbyK/vQkmnhqFeYbm5DkQpSpII0S1jqL9G8ZQu0tLTYtmYTiUFx2Do78MuWFbkGuVAqlUjET9fLTMuMLkV7cPDlPmKjYyhsU5ges3vQpFmTHOs7OTkxZtEEls5YhEGaDnHKBNwrFWPG/Fn5Ov+cOHTuMF1adWbqvTXIkCLVk7Nw69JseZ2+F/+1J/lq/nkEQWDFpg20rt+QS7e90ZLKKO/kxOSqjTPSDAl59OGAjz7csZ3UlEScCn/uw+x/x3KpgDIt6wd3fHwcdhYajGxoh5ZMRWq6wPrzqcRE53w/rUxPQybLusRVKpXi4OjIVWzZ/Icvck1NGrXuQJcevfJ+UYCZi+bTs11nZt0+iY5Mk8Jmzoyp3AOZRIpMkOTNh61aoKX90Yfv47F1tmf+1jz48LNnxiaa5rQt1ItjAXtIVMbgbupGv+Hdc/XhlJVjmT95CZpJBiSJcZSoU4TZi2bk6/xz4vj5Q7Rv3oXf7sxBEKVoGklZtW6h2of/Hq+BSGATkAwM+PxhjCiKuQeT+D68BC4Ai0RRvP5Z+UFBEGr8C+NR84MjiiLjx41h4aKZNG1eH4DKld3Q09Nk9uzpWFrZYO9gnK2eg4MJkZEZcVmkUilNmzaladPsueovX7rIH0cHA1CrdlH27L7GxEktEUURb+8n7Nh2GWNjY/bu3cu6dSt5ExBA+bJT6N17ZMbYxjenXHkXgoOjSUtTIpfLiI9P5saNl2jIZYwasYtlK7qhpaXB8+fBzJl1jLJlKyGTf8A/YDFyuQyFIo1aNebSttVKdHW1iI1NpE6d2uzZs+uL10UikTBh/ER+HjuOhIQE7t27R61atQrgiv+zqCegf0FHR4fR06ZxafVqhlWsmFnuHxGBlrl5np4UC4JA2/adadu+c5ZylUpFQKKKD3FJmBvofCwTOeoXzpCezbIcu2fjGiY3LYGtsV5m2WQ7ByZs20DT5tmTnNdr0QKvvQfoUarMxxIRn1eviIyPJyoski6DB9KydStksvz/yuVyOVN/mcq0rrNpY9gDiZBxExSbGkOcThSFCuVt9Uz9hg2o37BBnvt1dHQkWT+ZqJRITLQynkxZ61hjYWvJks2LsyXkzYkatWpQ7Xw1QkND0dPT+6Yn/Z+jr6/PMe+M4CUJCQlYWVlle+vwfVHfcKn5/ujo6DBm2hQurlzDkPKf9pO/joxAy6IAfJggEh6XhMVnPjz8JILB87Luf9+zZSXTu7pia/4pjccMu1TG7FxP0+Yt+St1mrbmj5NbGVD9057Pq0/fEBGbQEzEBzr2G0KLVq3/tg8nzZ3BwgFTGOrWPNOHUclxhEkSvqsP04ySiImNxEgjw4cWmjZY2VkydcfCPPmwZq0aVL/yfXx46uIxtQ9/HBaQcVEEQPdfHsuf9BBF8ernBYIgVBVF8Zooij/9W4NS8+MSFRVFaGgYBvraZDxHyaBN2wqsWDaf1avXM3fueEaOapwZ6TY2NomTJ+4zaeKXI7X/iYWFOW/efKBoUVumTm9D7RqzCQmO5uZNf2JjkmjStDRKZSJz501m89YBFCliQ63qs7G3N8XCwgBbOxNcXCwoWcqBKZMPMGdue6ZNOYCNjTG/7RtG394bcbIfgYmJHsHBMcxfsJDJkyZx/9Ec5HIZd+++4bc913Fzs+DlizBOn76IiYlJjrEVckIqleZ59c6PiHoCmgP16tfn2vnzzL52jUqmpoQqFNyNj2fxxq9HKMwNiUTCxHmLGf/zT9Rx0MdQU4r3m2jKNWpD8eLFsxwbFfEBG6OsT4+NdDVJTcp5yVurtm0Zf+kSi25ep4yxCQ+Cgrjs/5px1dtgpWXM+c2HuHLWm2W/rs1zzrvP8fDwoHaP6uzbuRk3pQcpkiReaz1jwfp53+1GQxAE5q6ew9i+P+OWUBgdUQc/wY9q7atSpkyZrzfwEYlEgo3N99mLZGBgUGA3cfmhIPPeCYKwBWgGhIui6JHDzwUylkw1AZKAXqIo+n78WaOPP5MCm0RRnP+x3ATYBzgBAUAHURRzfn2v5ocmw4cXmOtzlYrGZoQpUvBNimfxxg3f1K5EImHC3CX8PG44dZ30MNKSctY/lvINs/swJuoDNmbWWcqM9DRI/biE9a+0atOWiVcuMOPkIyrZ6OL7JhTvJ+/4pX0d7Ex0OHZkK+POn2PJmvV/24eV2tdl2f5DlNNxJVGVwr20t8xas/C7+vCXNbMZ1XscDvFF0VLqEqD5hJqdK6t9qPZhFkRRnP292v4GVgJ/fUqyKoeyfPG135ea/1309PQQRUhLy5q95+XLUKytrWjevDnr1q2iccMlDB5Si8REBUsXn6Fjpy5fXL76OQMHDmPKxBmUKuWATCZh0pRWTJm0n3Llnbl2YwbduqwhMVHBpavTKFHCHoAPH+IxMtShdp3i7Nl9nc5dqrBj12C6d12Hk/0I4uKSefR0AWZmBhw5NobAwAiePn1Px3arGTRwMCN+GomZmT4rV5xi8UIv+g+sg62tMdevvWTu3Fls3/7lN5//31BPQHNAIpEwc8EC/Pz8uHv7NqXNzRlVp06uydfzSqlSpdj6xwnOn/cmIT6OaeNr5Jj3za1oCe6/DaGM06fAHa/DY7GwzTlHnEwmY/GaNTx69Igb167hu3YTW9oMR0OakU+vU5FKrHtyCV9fX8qVK5fvcSckJODs7kSzQQ1Rpauws7ejTt2Z6Orm/HA1JSWFS5cuERcbh2dlT+zt7fPdJ0CxYsU46H2AC+cvEBsTy4Cq/b4aZTI/PH/+nPv37mFlbU3VqlX/1huRf4WCXXK2DVgN7PjCzxsDbh+/KgHrgEqCIEiBNUB9IAi4LQjCUVEUnwITAG9RFOcLgjDh4/fjC3LQav4ZMnw4P9OHZczNGV2QPjx08pMPx+TsQ1f3ktx7+YKyhT8FYfN/H4+ZtVOO7cpkMhauXMejR4/wuX6Nm9fW4TWqExryjG0K/asVY965h9/kQ0c3Z2Q9W6BKV1HE3o6f69b5R3z4x4X9mT4cXbVngfvw3r17WKt9+D/tQ0EQfgd+FkXxtSAIa4FqwGxRFA98rz5zGUtloApgLgjC55u2DciYqH8r28j996XmfxRNTU169upJYGAksbFSDA11CAyMYPzY/YwYOQWpVMrRoyfYsnULWzYdQktLiylT5tOuXbs8td+9e3devXpJiWITEEUROztjZHIpk6a0IuDNB06ceICOtkbm5BPAwcGU5ORUevSszo7tV2jVYik9elanRcuy+D0LRSUmo6+v9dnxZhgb65KamoZEIqFevVqsWH6SxQu9uPfwF+ztTbl//y2JiQrWrz3F/v376dixY4Ffyx+R/5FPl3+HIkWKUKRIka8fmE90dXVpnsMy2s/pN3wU4wZ0p69CSSkHM54FR/HrzRCmLv/yWwdBEChZsiRxcXHUti+aOfn8k5J6Vty7dSffN1w3fW4yd/RUSsud0RRk3E3xp/2Q7l+82fLz82NMnzG4prqina7Nbslu6napy/Axw/PV75/o6OjQtFn29fvfgkqlYsqYSbzzeYW7xIGbQhwrtZaxeuc6rK2tv97Av4goCqgK8Im/KIqXBUFwyuWQlsAOMSO8p48gCEaCIFiT8TT/lSiKrwEEQdj78dinH/+t9bH+duAi6gno/zT/qg+HjGHCsC4MqKukjJsxTwNiWXMqjskLFnyxzuc+bOrhmDn5/BNPGwPu37n1N3zow/xxk/E0sEZLkHIt+j1tBvbK1YfjB4ykhNQOPUGTg8u2UaNDI4aO+rF8OHn0ZPwvB+CodCFOdoVlRitYt3uN2ofZ+V/wodvHyWcFoBDQEDgD/OMTUEAD0CPjflP/s/I4IG8zhVzIw+9Lzf8wixYuYe/e33B1+gkbGxMCAz/QtVt3evbIiGWiqanJ4EGDGTxocL7bPnzkMBs2rKdIUWvOek+kQb35fPiQsapmyRIvXF0tCX4fhb9/GK6ulgAMH9GQt4ERvH+fxlnviSxdcoIxo3ZjZW3Pjh372LBhLWvXeDN1WqvMftav86Zp04xYMosWLaNq1SrUql0EOzsTRo/cye8Hb9G2fSVq1nKnX//e6Orq0qxZsxzH/P8J9QT0B8XBwYGlW35jz9ZNHLj8EKdChZn360KcnJy+WtfS0pKQ1OxL00IU8ZSxy1+akNTUVOb9PJ1h9i2RCVLSVEpqaZRl2dpdVK9TM1vuOFEUmTR0Em3lbTHVz9in5Cl6snf3XmrWr0nJkiXz1X9upKSkEBMTg7m5OVJp/h6knjjuRbxPKP3sP0UNexkTwJwJM1mz/et7B/518vfE30wQhDuffb9BFMX8rJ+0Bd599n3Qx7Kcyv/cKGgpimIIgCiKIYIgWKBGzd/EwcGBxev3s2fHBn7b9wBHlzLMWTkozz4MjEvJVh4Yp8DFNn+5L1NTU1kwcSqTPGojE6SkqpTUdynBrA3bqFa7Vo4+nPrTeAZYNsBCNyNYRm2xLOsOHuNh3R/Hh17HTxB+MYoOpt0yy97Ev2bmuFms37muwMb43VD78Es0A/Z/7PPr0bG+A6IoXgIuCYKwTRTFt//GGAAEQRhARh5UzM3NuXjx4r81lGwkJCSox5MLpqZmbNq0laCgd2hqyklPV7F+/TpcXFwz8x3/FZVKRWhoKNHRUQiCgLGxCVZWVplbLhISEngXGMiUKVNwcrbA97YmnTsNRSoVePVcSrEizSnqLmJhYcj5s8m8filDS0uOrXUdkpPlnDwejFSagKF+NRYsaI2NTca9dZ8+A3j+3I/tWxLQ09ckMUGBrnZlBg1yz7ymW7ZsJSoqnBPHBAq7NWH79n6Ze1jbtYvn5csXHDhwAFNT0zxt6fjRfl95RT0B/YGxsbFh7ORp+a7n5uaGytIQn/evqGTjiiAI+EeFcj81grH16+WrrQcPHmCjMmTLvcPEJyegIZGRKqgoYeXGuVNn6NWvT5bjAwIC0I7XxtT4UzhriSChrLwsJw+dLJAbLpVKxdJfluP9hzf6GJIgi2PAz/1p1TZ7MJIvcfKgF7VMy2cpczNy4rjfFRQKRbY8ej8a+dzzFCGKYvmvH/ZFcupMzKVcjZoCx8bGhrETZuS7npubGyn6Flx8HkTNwrYIgsDzkEiuhaXQv179fLX14MEDHKS6rLl+mvikBDSlMhSiilL2Lpw7fZpefftmOT4gIABDhUbm5BMyfFjdsDinDnsVqA/P/H4eXYxIkscweFz+fOi1z4sKupWzlDnru3Dx6Rm1D7Pzv+DDc4Ig3AXMgdKCIBgAsf/GQARBWC6K4khgtSAI2a6HKIq5L38oID4+ZNgA4O7uLv5IUUMvXrz4Q0Ux/ZHGEx0djZeXF0OGDuDc+UmUK+eMKIrs2X2NgQN/4dWrN9m2gyiVSmrWrIqDowajxjREqUxn0YKdJMRrc+bMeQRBoF27ltRrYMa8OUc4e34ihQpZMWz4Elq1Ls+G9d5UqOhCxIcENDRllCzlwNSpPsTEJKKhIWP9+k0MHjyYkJAQLC0ts02CK1WqxM5dO7l9x5eiRYvTo3sPjIw+pV9MTEzEycmeSp5O1KvvQa26DUlPV9Gn169cv/aCJs3KcOFiNDd9XnP0qBeVKlUiN36k31d+UE9A/5+yeN1q5k+bydE7x5AKEsyc7Fi65Ve0tLS+XvkzJBIJt14/pK9VfYpZZ+zNCkuJZon/IQopq2Q7XiqVIubwmasSVchl8mzlf4cNqzfwYv8behgNRiJISFWlsn3mLqztrL76H/XzcaaL2VNIqBD/VlCSfxTxH087EAR8vmnNDggmY2lVTuUAYYIgWH988m4NhP8jI1WjJgcWrFrHwlnT2PX7bWQCGNs5sXD95r/lQ58XTxhZxJOSphmBfIIT45jhe5ZeyuyTWalUiionz4gqZLKC2P4Gv67ewMPdb2mjNwyJICFNlcqmaf/H3lmHRZW+f/g+AzN0dyOiWNjdit2u3Wu7dsfaq2sniii2rt0dKHasrZiUSCiotMAwwJzfH8OCs6Diri77+y6f65pr4J33vOc5Z2bueet5nu1fx0NNzTztFAt5mJf+9TwURXGsIAhlgYiPcn/W/57n/Iy2Zz0vKaDzF+r/qd6+fUutWtUYM2YsgwY1oFIllb+7IAj06Fkbn3VXOHv2LK0e8RuuAAAgAElEQVRatcLf35+ZM6dy5cpVdHR0MDfXZvuO2dn82rNvGBXKTufixYs0aNCAkJAQKlSsTJOm7mzedIlf53WhY6eqXPB7Snp6JmFhMYSHxdC7Tx327f2dDx/kVK7iQkRELAkJ8Tx//pxy5crlabeRkRHDhw3/5HXp6emxd+8BfvihDY2buAOwc8c1goKiefRkITo6qgH1kcN36NGjCwEBIf9wVPF/Rv97V1TACgoKYvr4UXRr3YTJo4by9OnT7NceP37MpJGD6d6mMTMnjSEkJOSzbQUHBzN9wii6t2nM5FFD1Nr6WKmpqWxct4beP7RgQNd2HNi3F319featWMqeC6f57dxxvLdvzjO4x5eko6ODmYY+xfVztu5aaBtT3tAFSR4BKhwcHMg0yyQqJSq7LFOZye2M27Ts0BKlUsmBfQfp2qIn7Rp2Ys3KtaSkpHyVTUd3HqeeUePs9AcyiYz6Os3Y6rX9C0fmqHW3dvjF/I7KjUelJzGBuJQr9k2Cq3x3iUL+H39fR4HegkrVgYSs7WS3gWKCIBQRBEEGdM2q+8cxfySc7QMc+RaGfEqCICQKgpCU9Zz40f+pgiB8OVllob6LvsjDEV/Jw0kj6dHeg8ljBn+eh+u96NO5KQN6teHAfhUP5y5ZwW8nz7PpmC+rN237yzy0kOlQysQqu8xa14CqFvZI8hhQOjg4IDcUiEzKGW9kKDO5EO9P8/atUSqVHNx3kB6tetChcUe8Pb2/moeHdxynhl7TbB5KJTJqaDZny+r8R1Ns16MNN1KuqPEwIPEZrhWKFvIwt/4/8FADqAGsFwRhvyAIQwRBKJD+niiKd7OeL+X1KAibCvX/Q4sWL6RJUzf09bWwdzDL9bq9vSmxsbEEBQXh4VGfOvWMuP/oFypWsqVVm3Jqk2caGhIaNS6Jp6cn9erV5P379/ie8WfajPbs2nGdH/usxdbWhIcPXpGSqiAzQ8n6jQPZvu0qSqXI/UfzKVfOiaREOaCkRo2qLFgwT42ZX6MGDRqwefN2vFadJzExhX17f2f0mGbZg0+ANm0rIZWK3Lt37y+d49+uwhXQb6iAgABmjejPyFoOuP9QguevY5k3dhDjF6wmIyOdVTNGM66hI8Wru/IgNIKpQ3vx65rteSYfDwwMZMaIHxld146yXVx58TqeBeMGMmbeKipUyIlarlQqGTOkLzWNEvFs7YA8PZOtJ3xY/Owxk2b8glT691Yd4+PjKeVYjHcZCWgjRUAgVVRQ1MqRTEV6rvqCILBgzQJG9x2Ndaw1Okodggii87DOlCxZknkzF+C/L5TqOu2QSbTwX3uLgRd+YvvBzfma4RFFEaVCiaa2+kfXVGbK++j3+b6uRo0bce/GHbxO7sZN6sR7MZ5EUwWrF/w/8HcC/iLz8pQgCLtQzY6bC4IQAcwEpKrziGuBk6hSDgShSjvQN+u1DEEQhgNnUEUz3CSK4pOsZhcAewVB6A+EAZ2+ncW5JYqiWu4HQRD0gWGofH4Ofs9zFypv5YeHYz/i4c9DezHvMzycNbY341qZUq6xDc/D3rBwSl9Gz/LOzcNhfajjFI33YGvkikw2nV7J4hePmDR17jfhYZkiRYmSf0BHookESM5Mp5itPcpP8PDXVYsZN2AERWJN0UebR/IwfhjYlZIlS7Jg1gKCjgTTwrglWhpa3N56myEXh7Bl/5b88zBNRENLnYdGMjPuR7/L93U1atyIO+3usuPoJpzEosRrxJBmlcqaRV75bqMgVcjDXFqEajXWC9XW4CFAUWDCdz5vLgmC4M9ntiKLovi39qHn9X6Jorjx77RZqH+HzvmextunC1GROqzxvsHQYY2zfSVjYpLwPfuIhQvqsXDRfAYNqc/IUc0ACAx4Q2ZmZq72Dhy4hbOTJZOntibqTWlGj9zG/n2/Ex0dz+6dNzh5/D5OzkUJDwujZCk7WraqgKmpHiNGNWWt9zkiImIJDFnGk0cW3Lk/l0YNFnD48AGKFnWlV6++NGvW7Kuur23btpw7f5aypaehb6CZi/mCIKChIfnLg9x/uwoHoF+pgIAALvmdRUtLh6YtWmFllTMTvnH1csbVdaKknWqmpqSdGdMaSVm9cjEZGQpmtXLF1kQVKbFCEQvGCAKb16xkzpKVuc6zyWs54xvYUzpr1qeEnQnTmmuyYuUi1mzZnV3vxo0bOBBD52qqnEfaMk1GNirO8N0XiY6OzrYvIyODSxcv8uKJP04urjRu2jRfM9tlypRhkRhDZ9cGpKXKUSqVWOvpcSH0DM1qVc/zmCJFinDw/EFu3bpFUlISlStXxtTUlHfv3nHlyO+01x+cPTNV0bA2l4KPcOXKFerVq6fWjkKh4LzvOYKfB1K8TEkaNGyAVCrFysmS92/eYq6dE8fhSeIjarZW92HK1da58zx/EkRJ9+J4eDRk0swpRA6IxN/fHysrK8qXL//Ntpu9ffuWU8dPkZyUTIMmDShZsuSXD/oK5bFb7q+3JYrdvvC6iGowl9drJ1F1yP5cHgN4fBMDv0KCIJgAo1CtMuwAqny0Ba1Q31gBAQFcOn8WLW1dmrZo+VU8nNnyK3jovZRJbc0pU0TlR1PSyYjZXTRZtGoBazbtza5348YNnPXe0M1DtRNSW0uDMR0dGex5nujoEeo8vHSRgCePcHQpRuMm+efhsrRE+rhXV/FQFLHQ0+PooysMrJk3f4oUKcK+s0eyeTjqIx7eOH6Dvub9srlT06wmx8OOf5KH53zPEfwsCDf3Etk8tHa2JDb0LaZaOTwM+PCQWh2/jodTZk0mcuD34+HJY6dITkqhYdP6hTz8/moGlBVFMRNAEAQ/4CEFMABFFQjpu+lL71eh/r168OABU6aMx8/vMmZmJgwYOIjp02ZkTxSamJgQHRWPsYku+vraNG+6kIGDGpCYmMqyJWfp0bMnAQEB3L51k0VLWwOQkpJGcHA0cfHJ+Kw7T7/+9VEqRSZP2oVCkcHZ85PQ0pKSmqpg9syDFHezISwshkqVi5CUlMqD+88pX74CFy88Y/Gi44gimJkZMGvGQfyfLsTUVB8RkdEjt+NazJLBQ6qTmJjKqFED6dK1D7/Mnpvv6xcEgdWr1tD3x/7MnTuX5UtP07pNRWQy1dDs9OmHfPiQQcWKfytV7r9WhVtwv0Leq5bhObUvjjHHMAjdzbj+7fA755v9elhwYHZn6w85mhsSG/2ahJjo7M7WHyrrZEbwiyfkpdDggOzBZ05bBsS/i1Ire/74ERVtddXKBEGgvK0ugYGBgCpC1sAenbm7ZTlFw24RvN+Hvp3a8f79l1cMDQ0N6TCwG95Bx3itiCVOTGFX6AXMKjl/NoCGhoYGNWrUoEmTJpiaqnL3BQUFYS065erUWGc48/i++n2IjY2lV5suXF58AM2z0Zybt5s+P3QnMTGRSb9O5HjGfvzjH/BO/pab8Vd5YfaIHwf1IS/FxsbSoUV31k44w72NaXiPO0Hn1r1ITEzEzs6OZs2aUaFChW/W2bp08RJ9W/xI0LrnxO18x8xe01k2f+k3aRv4uu1m/6xvVIFJEARLQRAWAveADFSdr2mFg8/vJ2/PZayYOAC7V77oPj7E2D4d/jYPgz7Bw7CQF9mDz+y2rPRIjHmjVvb86UMqFVVf5RQEgQouMjUeDurViYd7F1Ai4QKvTnnSr2ubfPOwXd9eLL5/gbCUBGIy5Gx6chMj9+J/iYcOEodc3HHCiSd58LB7q26c/eUEaQcTOTbjEL3a9SQxMZEp8ybgp7GHZ0n3iUmL5n7SZV5Z36fv4E/z8IcW3fGecJq7G+WsGXeCTt+RhxcvXqJ7k378vjiUF2sTmdB5Nkt+XfZN2gYKeZi3FH8MPiF70FwgrgiiKL763KMgbCpUwSsoKIgGDerSpp0jb2O8OXdhPDdvHmfwkAHZdfr3H8KsGUfIzFBy9Pg4OnSsypzZh1gw7wzu7lXYvm0b8xdMJCQkmNu3ggGQSAQkEgkHDo1mx2/XMDMeiKXZYLZtuULr1hXR0lL9Phw8cIsiLpb4nX+C36VpnDozCTc3W4oWtaJr9xI4OZnjtdqX9j9UYeuWywgC2NiofoPi41JISEjhnN/PdO1Wk0GDPbh8bSpeq1cTHh6e61rfvXvH9evXefPmTa7XACpVqsS+ffuwtS1BxXIzmD5tH316+dCn53q2bdvx1VHN/7+ocACaTwUGBvL48gGW9XaldklTmpa3xKtvUdYtn0VaWhoA5lbWhMeopz95l5iCjoEx2nqGxH5QTwUQ8jYRa7u8E5JbWtnw6l1irrbS0eT58+c8ffqU6OhoHIq4EBiTO8XAi/fy7JQAW3y8aWom8FOtUtR0tadv9RL0KWbM6iWfzqH3sXr1+5Gxa2YT6Kbgjt17Ws/oy6/LFn51B8Xe3p4YIfcXMEbyhiLFnNXKVi1aTj1lCdo61KW8pRs/ONajSqojPp7elCpVio1HfTDtqkNg6YeUG+HGtiNb1KKMfazlC1djFFmZMrrNcDQoQxndFmiHlsFz6Zqvsj8/UigULJqykB/NelLdvCrlzcvS27I7Nw/c+KTP2tdKRBV0I7+P/4hCUPlcbQJSgUGCIIz741Gwpv3vKTAwkEfnD7O4vTs1XS1oXNoGz46lWbt4zl/m4cu3idh8gofm1naERn1Qbys+lTRRqs5Dp6IEvM699epFZHoOD9d709JZzogmLtQuZcPAhkUYUFELr2UL8nXtPfv+yPBl87lnrYWfbhqNxg1hzpJFf4mH0croXOVRYjTOf+LhyoUrqJJcjmaWHpQxK0VLy8aUji3OupVrKVWqFFuOrcO+l4zXFe9RbVwxfjv6OR6uwjCyMqV1m+FgUIbSus3RDi393Xg4b8JiWmgMwN2wBiUMK9Bcuy8Xd98q5OH31fqs3SAACIJgDKwvCEMEQbia9fyHj77ac0HYVKiCV69e3ejXvw6DBnugr6+Nm5st+w4M59DBQ0RERADQo0cPmrfoyOPHEbRp5cnC+ScxN3dixIgxhIb68yJoEb7nx3P81HjmzT3CsWP3kMk0aehRiv17b2FioodrMRumz/yBqtVcefIkIvv8ISHvkEgEunWvibu7Axf8nnDt6gtu3v6FESObcu3mLDQ1JbTvUIUPH9KQSAQuXXoGQFJSKt171MreDgxgbm5Ao8buaulQMjMzGT1mJMWLF2XM2AGULl2CPj/2ZM+ePRw9ehSlMmdOSFNTk/37D+PtvRWpZjmqV+vE8+eB1K1b9zu/EwWnwi24+dTlC+fwKK5BaEgQWhqgFEWUaFLOVoOHDx9StWpVeg0ZwaJZ45jRrCQWhrrEfpCz8NwLeo6aSXq6ggU+8/i5RXGM9bR4m5DC0nOhDJ+zOs/z9RoyikWzRjGzZTEsjXS5E/KWcVuvUNLRjBl9mvA6IR07OwdsXEsTHSXiHhBF9WJWKEWRI/fC0bYpnp0j78aF86xqWkqt/eoutmw4cjvf11+hQgUqVKjwl+8fqAJyOFe25d6NK5TXq4mAhJfJL4ixeEWjP6WHuX/9DhMdO6uVVbEqzbLzB2HaJGxtbRk7eUy+znv94m2q6aknKXbWr8AlXx+mzf5bl5RLT548wV60RVczJyy3IAiU1yjLhbMXKFWq1GeOzr9EZeHc0Z+0EJWvkwDofaFuof6mLvudo6GTNq9CApFpCCoeChqUtdTMNw8X+sxjSvOPeHj+MzwcMJr584Yxp7sjliba3H7+nlGrb1PSyZxZAxsSGZeJvZ0DVi7uREdLKef/jhplzFEqRQ5eiUZmWiqbhzcv+eLTTT0AUc0SlqzddCvf1/+teGhX0Y7r969TzbgaEiQEJAYQaRSei4f3rt7lJ5O+amUVTcuy/tx2JkyfiK2tLeOm5I+H1y7eoareELUyp+/IQzOFA9paObt0BEGgaHpF/M4U8vB7SRTFNYIgaAmCUA7VGP2FKIoF4tArimLtrGeDgjh/of59iouLIyDgOVOmqbsZ6OtrU9zNmoCAAOzt7REEgfnzFnLu3Dnc3H7Fzs6OsmXLUqNGZWbPbY+ZmeojVbWqK5u2DKJPT2/S05Voampy9UowRYuacfPWbKRSTYYNb0wxl7H4rDtP/wENKFvWAZ+156lbtwQA06ftp/ePdUlKkuOzzo+3bxOpXdsNby9fRo5qysD+6+nS0ZO58zqjLatKRHhMruuKjIxDqVRy7949SpQowWqvVdy960dA8GJMTfVZseIUs2Yc4OyZEyiVShQKJWvW+NCtm2oXuSAI1K9f//9lSpW/osIBaD6lFEUiIqNpXswWzaxZj1RFBkEhr2iVFcq/atVqpP88n1meS5AnxiHTM6T3iGk08FC5foiiyMQNq1GkJKFnbMaAnxd9shNTuXJl+k1ewOxVS0iKDyQkJIS1fStiJKRgb+xEeFwq046EUs/QhksSJy6k6rN2+x2QSKhevwlzR+Us+khlMlIVGUh1cpbx0zOVSCT//LL+Yq8FeC5ezcGja1BmipSrV4YNs9bm8r+SaGiQocxE9tEMkyIzHZnW10dk1JRpkpmSgaaQc2yGMh2Z1rdJC/OxdHR0SEORqzyNNHT08k6Y/NUSvzrv3f+8RFGcU9A2/JekFCH8TTRNnRyzZ4HligyCXobT5it4OOkreJg2bglT1ywmOT6c4OCX+Awti5FGMg5mVoTHyPl512saWprjJzhzNkyP1SdvgyChWp1mzF00PrutbB5q5vAgPUOJUADbnBauWsiqJavYeHwDykwR96plWDdjXS4eamjmwUOlAulf4KFUpvEJHn77aLc6OjqkC2m5yhXI0dEz/zYnKeRhLgmC0ADYDLxCNQB1EQShryiK5wvYropA7SybroqieL8g7SlUwSggIAAdHW2uXwugZcsc5icny3n6JJxixYqp1dfU1KRRo5xJudjYOOzsVAv8CQkp7Np5nZDgt4CEmzdvUqpUKbp0+YG27e2QSlXDHG1tGWfOTcGj/q/MmnkYLZmUpKQ0tm65TL8B9fF/FIaDgynlykymVGk7Xr+O493bRDIzlRw+fBe/S9NITVXgueIMHg3Lsdb7HF2716R8eSdEUWTTxos8exbO2LEjsbe3ICLiPVpaMg4fG4mpqT4vXrxh+s/72Ll7GK1aq3w6d+28zoABffHw8MDS0pL/mgoHoF/Q1SuX2e6zkoDnT5DHR/FDJXOsjFUdrDfx6TyPTFWLXFWrdh1q1a6TZ1tNm7egafMW+T73H235+fnhv3ceFnoaGCBDEMDRVIdqzvroyzR4H/qcuftOoa+vn2c7LTt1Y9vRrQyrXSZ7m9j+B8HUa5GT/1mpVLJr228c3XUAhTwN96oVGD5hzDf/UmhpaTFh2jgmTPv8rsjmHVtzdu8tWjnWAlSd1VORN6jauhYj+w0n9MVL9Iz06T3sR5q3bP7Zttp3bcHp1Rcord80u60XqX50Gv7t81+/jnzNrdDbPA94hpWeFc2KNMFCx5y7mQ/4qfWIb3SW/5QvU74kCMIBYIIoiiGCIKxB1cmZI4rivgI27X9KV69cZpu3JwHPniKPj6K9uxWWhqqJlTeJabyITv5+PKxVh1q1VDx8cuQXLPQlGGpoqnhork21Yjroa0uICXvK3N1nP8nDFh16sMnXh1HNXLN5uPtmOPWats2uo1Qq2bV9Oyf27iU9LY3SlSsxdOy478LD8VPHM37q+M/Wa9GpJZe2XaWxVQNAxbDz7y9TvWMNRvQdkcVDPfoMzw8PW3Jq9UVK6TfJbivgO/LQP/J3AlOeYaZjRS2blpjILAnSvs3MNoO+0VkKeZiHlgINRVEMARAEoSiwDyiwaCaCIMxAFf33j6jkWwRB2CeKYv6jthTqf0JOTk4kJcnZuOEiRYta0b1HTSIiYhk5fCsODg44OOTtivGHGno04rft1+ndpzbNGi+gSlUX4uKSkUhE6tevxYABg5FKZSQnqya/FIoM7twJQUdHho2NOT//PI/y5ctjYWFBw4Z1qVltJg6OZpw6+ZBu3Wtw7WoAS5f3pHhxG+bNPczDh2FUrqyKzl6nTgnOntagWHEb6tScjbt7EVJSFERFx9G+fRVWePZAR0dGYGAU5d0n4+SkmmibPnUvrVtXpHWbStnX0b1HLbZsvszMmTPx9v7/kYHhW6pwAPoZXbzgx27PqUxv54xG/eKcuC7Qfrk/LSpYkakUePkug3rubiQnJ39XOz58+ICJtgRlZgaaspwfWlNdTZJSFRhqa5KamvrJDleHzl1YGhTAiKMXKG1hQGBsMlYlyzFzyNDsOsvmLeKdnz9jijRCW0PKw8AQfurel21H9qKn98/vaOw7uD+zQl6y4uYBHGXmhCre4lzJjfP7fWmr14yWZg1IUiSxY8YWkpM+0LHrp6PaDxjcl5CgWdz0W4+BaEeiJIKarcvR+8ee39Tm0ydPs2maD5NLjCbpTSIx8lh8Hm3AyNmE6YtnYG1t/c3O9T8alfvvqFjW4LMK4Ao0Bc6i6nQV6hvoop8fu5bOZLJHCTTKlufU3Rd02nCb5mVsyFAKvIpPo5578X+Gh7oCSmUGGrKcwa6pngZJqekY6Uo/z8NOXVga9ILB287hbqvDi7dyLF0rMWNwTmDT5QsXkHzrFnMrVUBHKuVOeATDe/dm84EDBcLDfkP6MyN4Bhuub8dWYk1E5huKVnPl/IFztNZuTjOThiQpktg+fSsfkj7Q6Ys8nMnvfhswEG1JlERSq3XZ78JD70mbGOowifjIJGLTYjgQsgYTF2N+WTatkIffVxp/DD4BRFEMzsoNWpDqBlQQRVEOIAjCAlRB4woHoP8xWVtb88MP7XgZep/ftl/lp8GbMDTUIVMJ53wvfPZYpVJJ1SrVGT9+NPv23GDchJZs33qFSpWLsMrrR9LS0vll1mFeR2bw4EEsBgY6TBi3AxsbY+LjU4iJScbNzQ03NzcA7t/3Z/HixcyaNR0rayMOHrjDjVuzcXAw49q1AIq6WnHvXmj2+detPU96Wlk8PErTslUFNm+8QukyFYmMvMnyld2z83gWK2ZN2bKO7Nl9g2HDmxAVlYCHR+lc1+PmZkN4WESu8v+CCmQAmuUQvwEog2orRj/gBbAHcAZCgc6iKMZl1Z8C9AcygZGiKJ75XrYpFAp2bt/MhTMHef7Unz51HDE31CY5OYO6JYwobavLqnOxjG9bgyLmhgzZ9wR3d3eUSiWXL13iup8vhiammFnbcOnUURLi4qhYvSY/Dh6GhYXFV9kSGBjIFp/lBDz1JzI0GMcO7mgaZGBhoEGmUuRCQAKjW5ci8WUm5uZ5b2dKSEhgy/o1PLhzFU0DPWQV6jG7a3dsbW2z6yQmJnLj9AWml2qfvSJQ3qoor+XxHD96lE5durBvz372bzmEQpFO4zYN6De4L4mJiRzYfYDoiChqNKxJ4yaN0dT89Efq1q1brF++iTcRUZSpUJqh4wd/Mhm8pqYmc5cuICoqirCwMJydndmwej0NZbVwMlDNjhnIDOhs3ZYNa7bRoUvHTwYB0dTUZNHyuURHR/Pq1Susra05fewkXZv9gKZUk9Zd29O5e9e/HWls/TIfulu2R0+qh6WBBbapdugm6hNdOQ6Pxt82Av9/KJjG16oVsFcUxTeCIGQUtDH50b+eh9u2cP7EEZ4/eUTPqq6Y6euQkpxJ7SKmlLTUw/t2JONb18TZ3JBhh/y/Lw/XLyfg2SMiXwXj1NMNqXEGFkYyMpUifk+SGNdRn4QnqZ/n4UYvHt6/gqauARI3D2bOyM3D277nWN6gXjZTqjg6EJaUxImjR+nYpQsH9u7jyI79KBQKGrRoTJ+B/UhMTOTQnv28ff2GavVq0ygfPNy0ciNRkVGUrlCawWOGfJaH85bPy8XD+pq1cfyIhx0t27HFaxsdv8jDX3PxsEuzH9CUSmnTtd034eG6Jetppd8dXU09zIpbYpdqi2GyLim1owt5+P11WxCEzcD2rP/7APkP+vB9FApoA39EH9MCggvMmkIVqNau3cDkKRPZvEmVA14i0eDDhxT69+/N5CnT6d6tO6mpqWruCEqlkh49uvDs2T0GDWnA8qUnMTPTx9hEl7U+/QE4deohpqa63Pr9ES4ubgweuJEz5yZTo0YxRFHkt+3XaNOmJUFBL5HJZAiCQJoiDWtrYz4ky7G0NCQ8PIb6deZga2dC6Mt3ZGQoOXPmERUqODF1yh5272nG8FEqv83xE1pS3n0qpmb66OpqqV3j0BFNGP7TFqKiEile3JqdO68zdXo7NDVVbJXLFRw8cJv585f/Q3f936WCWgFdCZwWRbGjIAgyQBf4GTgviuICQRAmA5OBSYIglEIV3bI0YAucEwSh+Mchxr+VRFFk0tjBlDUJxmuQNSFBFlx/HMPPW67xUx1LJAhYGGjwPiGJJ4Gv8Lwk0HvYBLS1tZk0ajh6UaF4OFtz4uxFrr57w89tq1HEpiSXAx4zom93fHYewNDQMF+2BAQE8Mu43kxoY06Zpk7c8oeZ2+5R39WCYuZaHH8Ui6mRKYsuhDNpgWeenY20tDRGDOhOxxIig3o6kZCiwPvcUY7JNBk8fHR2vcjISBx1THO1UVTfiqCnAcyZ+iuBR95QV7cTUokU//W36HCoExpykRoalbDRMubklSPs27qXtdtz+y8BnPc9z7IxXtSXtqWGlhWhFwIYdGMYGw+vxc7O7pP3wdraOnumPPBJIK0MGqq9rqWhhWaaBLlcjo7O530srayssLS0ZFCPfji90WGEbUsylBmc2XCepw+fMGfJvM8e/yWlJcrRs8pZHdHR0aakdnFuBu76W+3+WX9EfSyUms4JgnAXsADKC4JgCCQUsE351b+WhxNH/kQpZRTLmzgT4qLgZmgsM/b4MbCyg4qHulJi4j/wJCiUVddEeg/P4aF+dAgNi1pxwvciV6OjmNy6Bi42pbkS+IQRP3bHZ9dX8nBiLyZ2NaZMJxtuPUxnhs8jGpYwo7illKP3EjAzNmb+ibdM/BGU7T4AACAASURBVHX1J3k4cnBXOldLZehoCxKS0/E6vJ9jhzUYPDQngE9kZCRFjAxztVHC1JQ7z58zb8Yc4q8EMsChLjINKVeO+9PlSAekaZk0Ni5BKR0jLt/dyYHfduO1Zf0neeg1aRVtTZphqW9BwJ1ghnX5ibX7ffLNw4DHgTTXVw9WpKWhhYZC4+t42L0fDq91GWrTigxlJr4+ft+Eh6kJaejqqngooPIHLa5dkoMvbv6tdv+sQh7mqZ+AwcAf25wuAwWyx08QhFWo3qY04IkgCL5Z/zcGrhaETYUqeGlpabF82UpqVK/J+AkjWbe+L3XquHHjRiC9e4xk1sypvHoViZ6eLqtWrSYtLY3du3fz9Ok9btyajkKRwaqVpzlz+iFNmpZFEATGjvkN3zOP+GlYY6rXcGXGtP306VuXGjVUPqWCINCrd202b7rGqVOnaNtW5Xbx5nUk/QbUZeWK04RHxNC5oyc7dg1DLk/n1zmHWby0O507eGJnZ0LtOm5oyTSBdEAVOKnfgDosXXwaf/9w3N1ztg8/ehhOx06dSUrUJyQ4iri4FBo1nMeESa3ISM9k3q9HMDQ0oW/fvrnuz39B/3jouKyOYV1gI4AoigpRFOOBtsDWrGpbgXZZf7cFdouimCaK4ksgCKj6V8797t07rl69SkhI9s4UPnz4wPXr13n8+DH+/v7oyAPo09QeXW1N9HW1+LGRGXJFIqmpYG9sRHSigIaGHnvvx9Fu8HhatmnLlStX0I0KZXTtcpSwMScwKpoVbcrxMuw1N4NfU9HJgtbOuhzatydPu96/f5/Lrk3eS5nc3gJ3FxMEQaBaWWdWj6zKxTdSrgmVsarcGvu6nekxfOIntzKdPnWCWjYKmle0R1NDgpmBNj+3dePyqX18+JCT0sDe3p6w1BjEP+1jCkx6g5WjHbdO3ae+QRt0NfWQSmRUMKjFq4evaC9tTmWL8rgYOtPKqjFGobqcOHoiT1tWzfOmhU4PzLWtEQSBIvpuVEitz0avzfl+/9zc3XiZGKpWJs9II1NLiXZW4JMv6c6dO2hFpNHEoSpaGlL0pDr84FSXkBuPCQsL++LxSUlJXL9+nadPn+a6XzrGuiQp1FNFhCSE4lamRL5sy7dECvPe/UmiKI4F+gLlRVGMFUUxURTF+gVs1hf1b+ehVuwrelYvhq6WFH0dLXpVdiQ1ORm5QomdsRFvk0U0NXXY5x9D+yE5PNSLfsnoemUpaWNO4Jtolrcqz8vwCH4Pfk0FR0taOel9HQ/XLWFKdxPcXY1UPCzvxOqJFfEL1eFyai0sy7XFtnpPuv80+bM8rF3sAy1qWKGpKcHMSItpvRy5cnZPLh6GJCTm+n4/iYnB3N4e/wu36OJSH32ZLjINKQ3tK/D6aTD9rGtS0740xc3s6VK0NrbvBE4cO56nLd4Lvehu2QErXUsEQcDN2JV6klpsXrMpv28fJcqWIDQpVK1MnplGplbmV/FQGqGgkX01tDRk6El1aOdYj+DrT/42D/VMdEjOUE/D8yo5hBLubvmyLd8q5GEuiaKYLorialEUO2Y9PEVRTC8gc+4Ad4FDqCbWLgAXganAqQKyqVD/Ei1YMJe1Pj/SpIk7OjoyTEz0SE9PY+Hi9nxI3cid+7P58CEBKysrhg4dzI/9aqKlJcXAQIe69Upy9Mg97twOwd8/nH17bnL1xiyGDmtM1WqupKdnULRobr99R0czYmJiSEpKYtasWWzfvo39+25z4tRERFGkZs1iNG7sjv+jcBp6lKZOnRIEBC+lUmVnUlNyB5lUKJTUqFmTNi2Xs2H9BS5ffs7Y0TvYt+cev86dxypPLy5dusHLkAisrdwZOngbo0bspHSpOjx69OyfuM3/ShXECqgL8A7YnBUi/C4wCrASRfENQNb2uT8+NXbAx1OmEVll+ZYoiqxc+Sv3HxyhfCVNXu4XEShOjWqNOLrHi2olpcQkitx+nkb7yh/l9bGwIvJNOBVdtQl5n4ogCKz2fcsvbZoRm5zKsxfPgHbc8DtHQycrAN5/SMFUV5PJp+5jbixiJ3/LpkupVHR2JM7/QS67PJcs5o7vKcqZG/EqKRUsbFmwajXhLwMo1cperb6roymWZoksWuHFjMmjePXoODrvz3BsmwKnMvWYMv1XtQAgAY8fUM9RPfK5RCJQwlqHsLCw7BD4BgYG1GndhO2nL/KDc3V0pVrcjQriAW8Z4lIEa9FJbTUgOSMJU0yQZarP7Jc3KMOlUxdp37G9WrlSqUQen4autrpPVhG94py7m//Vwd6D+jD4zAD0EvQoYuhEgiKRw+9P0XdKv3zn4Hv2+ClFpVa5yl1lNgQFBX1yCxzArm072O29jRLa9iRkppBknMnyDauzA5MMHj+EtVO8aG/aEjNtU8KSIjgjv4DniLxTS/wt/Uc6UvlVln9TDaCJIAgicA7wEUWxQJKvf4UKhoeL5/Hw6kkqOuqw/306GLtQvU4Djmz3oYqDHrGpmdwNS6S1S84KpbmlFa9fR1DezpCXcckIEgHvm2HMbtuUuJSPeHjhHA1cVOa+T0rN4uE9THU1sYtLYOOF21Qs6kB8HjxctWQRd86doryVIa8SU1Ga2bHA04vw0ABKdVPvSBRzNsXSXM6i5V7MmDKKV/7H0Ik9xbHf0nEqVY8p0+ep8/DZPRq6qq8KSiQCJRxkuXhYq3lzvK9eppe7O3oyGTdehXE96QMDXVxw1bFW401CWjJWUgN0RPWf02oWxTh/xo/2HX5QK1cqlaQlpqFvpe5LWtzIhV0PDufnLQSg96DeDDo9EN1EXYoYOJOgSOBY7Cn6/dz/q3joopF7wO4i/TIPd27bya7V23HVdCJRTCbFTMHKTauyefjTxMF4jltLE532mGiZEZkSxlXhDGtGeeb7GvOtQh6qSRCEEFQLz2oSRbHIP22LKIpbv1yrUP9VPX0aQJ06OTtQFi88xqQpbWjTVhWsR0tLk5SUNBISkpBKJcTG5sQYaN2mIoEBb7hxPZBfZh+kXfvKGBmpUj5Nn7qXTp2rs3fPTUaMbJodrT0hIYXTpx7QvdtE3NxcyciUs2x5D3bsuMbsmQdp27YSBlmB9Vxdrdiw/gKiKLJs6Un277tFenomH5LlXLz4lEMHbpOcnMaxow/x9b1AXFwc3t6ebNvyiJq16vD77xvU3DsMDQ3Zt68wLMUfKogBqCaqSGwjRFH8XRCElai2l31Kef2y5BlyQBCEQcAgQO2H8+TJY8QlHma1j032D/OhA09Zs/I8Zz2rIZOqPph3/KOYuCKAAa1dAdA3MCAj04bzjyKIfZ+Jm7UFoxo2xtXSjNNPgjA0NQXAyMycmHeBAJjo6nAv8i2reztT1lEPqUyLIR4iQzYH4OhUU83es2fOEH3Nj+VNamTbdS7gJcvn/4qFtT0v33ygiE3OoO1dvBwtfRPWr11JGb3ndB+Y81uy6sgV9u3ZSZduOYEkHIu68fzuTSoUzfGHEkWRoHdybGxs1GwZNXEc+x334vXbHuQpqVSuXR3vUZtISUnhveSNWl2pRMYHMQmZTD2NSaIiEWPz3MnPJRIJGroS0tLlaGnkzMy/Tn1FkWrOuep/Sra2tqzatQavhas49fgCRqbGDFg4BI9G+fcncnJx5lGmX67y8IyYz0Zee/LkCUe8djPBtTOaEtXXJjDuFT+PnMiG3VsAaNSkETq6OmxYvp6Y6PcUcXNh2aQVuLi45Nu+/Ok/lVA9v1oEOABeqJgxBCgKTChIo/Khf56HJ46T9PQMPn1LZnPnwPUQvBZO58SEZsiy/FPuBkQyZfddfqxbBgB9fQMyLG24ePop8fI43KwtGN2koYqHT9V5GBsQAICJnjZ3w9+xvG1J3G0MkWppMUApMuLAI5yKVFez9+yZ00TfPI9n6yrZdvk+C1Xx0Mqel5FxFLHLGbS9i0tDS9eY9WtX4m70hO4dcq7R8+AV9u3dSZeuH/GwSAmeB16ioptJzo0TRYLfKHLxcMT48ex3cOCXXbtIS02lYo0arF64iJSUFLamvVerq60pJT49GemfeBgvT8bYyYQ/SyKRINGWIM9MQ1sjx2co7EMERSrmf3xga2vL6t1erF64Cl//CxiZGjFw8uCv5uF9Ze6gH5GZ77/Iw0Oeexhm2zubh0EJoUwZPomNe1W7Who1aYSOtw4+SzfwPjoGl7JF8JyyrJCH/4wqf/S3HtAF+EZ5wP6aBEEoBswHSqHyBQVAFMVv/YEo1P8jlSjhyvXrgTRqVIb5847ge/YxPw1rDKj4PHjgBhrU7wlIkEgE1q45R7/+9TA11WfuLwdxKWrFmrX96NB+OWmpOYv8ly4+52XYCrp39aJl80UMHuLB7zeD2LL5MhYWlowePZy+/Wuwa8d1+vavR7ceNVnv48euHdcJCXnLwkXdaNuuEtOn7qV922VERMTi/3QRjx6FEfg8miVL1zJ0WCOUmSLa2prsP7CXX+fOp2HDhp+40kL9WQUxAI0AIkRR/D3r//2oOlzRgiDYZM322wBvP6r/8S+hPfA6r4ZFUfQBfAAqV66c3Sk7cXI74yar+zhWrynwm08KUs2cssplrDExCmanbxhdPVSnvPJEzss4Q+Y0rUtlJ9VMRnxKKoeCo/Aop6BL88akJ3/gVVg4WhoalLa1xN5MGzszKUpUe84zlJm0r2rKncwUNXuP7d7JkLLF1ezyKObM3hNXmLJ4OfN+Hcac7g5Ym+oQk5DG3H3h9Bo8D58Vs9k8XH3Ro08jO8bs2KU2AG3Rqg2Ddm6khPV7yruYkZ6hZNuVV5SoVB8TE/WOkSAIdOrWhU7duuS6r3blLNh8cjFyeQoSQUCqqY3CIJ0X6UFUQzVLlZaZxoXka8zts4D09HRWL1vD6QNnETNEnN0cadmtGb7e+/HQbY+Oph4xaW+5ITnD6hHL8norPylnZ2cWey/9qmM+Vu3atVmrv4o70c+oaOmGKMKVqIfouprnyj31sY7sPkhj0wrZnS2AYiZOnAy5w7t377IDqtSqXYtatWv9ZfvyK2Vh3rs/qxlQ9g9fSEEQ/ICH/PsHoP84D08e2MGUeg5q3KnprM1m0pB+lGeyUnE7TPQesef3ADpVUX03rocl8EqhxZxmtdV4eDg4Co/y6XRp1hhFcjJh4WFoSQRK21tiZ6yLnZE2SgQVDzMzaVPGmgfpcj7W8T27GFqxqJpdjUo4sevgFX5etJxfF/7E3P42WJtrExOfxpxtb+jVfz4+K2ezZaz6Kt6PTa0ZvUl9ANqiZVsG995ACcc4KhQ3Jj1DyZbTURQv1zBvHnbtSqeuXXPdV2M3O2Zd3EiqPAUJAtoybZK1RR4mhlPXSDUBJ89QcDTqHjPmLiM9PR3vFV6cPXQaMsGhuCPNOrdg/+ajtLdoiZ5Ul7ep7ziTcoFlw1bk9VZ+Us7Oziz5uzw0WMXdt8+oYKHi4dXoB+gV+zwPD+86RF3damo8dDVyxi/seiEP/wUSRTH2o39jgSWCIFwGCjJf8mZgJrAcaIDKZaLwjfsfV1paGgcPHuT+g3sUdXGlW7duar7/k6dMZ8jAMQwYVJdtW67QomV5Lvg9pUIFZ7p2XsX1awE0qK+qK5VqMHBwQyqW+xlnZwuKF7fhwYNXSKUaXL85G7di47h48Sl16pRAS0uT16/jOXp8HJs3XWLm9P3ExSUze05HpFINxozaTuvWfTh04DaCIKCjI2PkqGaMHNWMtq2XUrnCNMq4OxAdncD79x/Yf2g0trYmREXFExwo4aH/fIyNVROig4Y0pGzpqfTs0ZuSJUsWwF3+/6l/fAAqimKUIAjhgiC4iaL4AvAAnmY9+gALsp6PZB1yFNgpCMIyVEE3igG3vuacCkUaOrrqEf0ERDQ1/8Q+AVxdnHiYWJbjSx4CUKlGI/adGMDCGVPZ+/x3jLRlhKdmUKZmHSL8TrDCoyw6MimBkVGM3n8OJwd7RIlAqqhJWooSUlKQyrRwdHLgfqT6+RSKNLSl6m+BIAhIBIGKFSsycNJyZnotIiXhJTJdI3oNnEP9Bg1Zt2wGmhrqbWnLJGQo1PemGxgYsHTdb6xaPJdlfv4IGpp4tOzAxIE/fc3tQ6lMx92gGC46LghIeK/xlgdWD4l1T2bdg60Yaxrzlvf8NGsYpUqVYvqEmbw/nUJXg6FoSDV4/Tyc46HH6DmlK/s27SI1SY6FkzmLZs39bCfne0hDQ4M129ezcsFS5l3dgyARqNO0PovHjfrscWlyOVoauSeQtSRS0tP/YdcasTDoRh5SfByIRxRFURCEf/v22wLioRxdLSO1MgERTY3cIQGKujjxVNuFfvtU22Wr1GnI/jOeKh76/o6RloyI1AxK16xD+LkTLK2vSlsS/PoNYw754uxoC4JAChrIUzMRUpORyrRxdHLgcdKfeShHR/onuwQBDQEqVKzIwHErmeG9kJTECGS6xvTqN5f6DTxYt2Jm3jxMz83DJV47WbXsF5YcfIQgkdKweRcm9h/K10iZmUFFMyfKatsiABEZ8fhqvCLUUeDak8OYyQyITI9n0JSRlCpVilmTZpB2NZbR1r3RkGgQFh3JwT3H6TKmB7u2HiAtQY65rQVzl84rGB7+tp6V85ey6NoukEiyeDjys8epeJh7t4tMkBXy8F8kQRAMAKUoisnAakEQJAXolqAjiuJ5QRAEURRfAbMEQbiCalBaqP9BxcTE0LBhXczMpXg0cuPwYT+mTJlI9epVqVa9FkMG/0TXLl1JSkxi+vRJ9PmxFut9LiCXK9i54xpvoxMZO74FEokAiAiCQJUqLox4sZg+vdbSuk1FuvWoSYO6cxg0xIMePWrSttVSTEz00NPXYvyY39h7YBQdO1Vj2s97efh4AXZ2prx7l8iEcTtxL+tASooCX19/Gjd2ByAzU4lCIWJp6czLl1E88F9A3Vq/4JLlVnL86D2KubbA2DinP2hmZkCHjlU4ceJE4QD0K1RQUXBHADuyIj6GoJoJkwB7BUHoD4ShSliMKIpPBEHYi6pDlgEM+9qIj/XrtePo4XX07J3j+/fypYTUFHUfxrexcl7H67LdZ7Wa7xDA6s3biI6OJjk5GUdHR7q1aIxXs8rZ29WK2VnzSzsPTqbqEBVliJapMQ4WOdvFfjsYTL3ubdXabNCiFSdPHaRHxVLZZY9fR2PvWhyJREL16jWpXj23T1CpCjW4/OgR9crlpDE4djOKGvVb5apra2vL/OVr8nOb8lRoaChxz2LpUaxHdllxXEl5n0qTtk2pvqA6cXFxODo6IpVKiYuL4+65B3Q1/AmlqORR3B2exTwkLiORp0+ecsjvy/vf5XI5B/cf4tLp65hbm9Kzf9dv+qU2MTFh1sKvSz3WqE0zdl73wtUkZ/HpXUocqXpiru17/4gKZ/z/rPWCIJh8lKrEGFhfwDblV/8oD+s1acOhWzvpUz9nu2dwrJKUTPVJurcJKbyRS/ltueeXedi8CZ6NqmbzsKitDbNaN+J0uhZEmqBjZoO9WY4/+u6zT6nXf7Bam/Wbt+a47z56Vc0J2uUf8RY7V7ePeHiEP6tU+RpcfnifeuVzXA2OXX9LjXptctW1tbVl/pK1+blNeSo0NJTkkGgGlm+RXeYKxL5UUr11c6rNy83DhxfvMdKhF0pRya2oB9x/84QPafE8e/qU/b4HvnhOuVzOof2HuHr2GmZWZnTr9x14uOjreNi4bVO2XvbBxcgpu+x9aixpBhmFPPwXSBAEJ2AHUAQwEQThOjCogH3i5YIgSIBAQRCGA5FA7ggxhfqf0cxZ06lZy45VXr25dy+UlctP07N3DWrUKMbFC9cpW9aLEiWKc+/eAzQ0wHPlGeRy1c+ZNGtxZtz4lmzZmIiGBigU6Ywfu4Pjpybg0ag09+6+ZMOmQVSq7ML2bVdISpJjYmKAoaEJv8xtge/Zx7gWGYO1tRElStphZ2eKr68/XqvOItPSZN1aP5at6EnXzqto374KrsWs2L3rd2ysXYiMfMHBI8Nwdragdh039u/7nabNyuK58gzLlzfNda0JCano6ur+o/f3/7sKZAAqiuID1H0U/lCeziuiKP4K/PpXz9e5c0/Gjj3PktfBVK4qEBwkcvOaPjXrdGOc53WaVlEFITp+Q8mUX9bm6mz9ISsr1QBWLpejI5Dd2fpDrpamfHj0mhnzVzF53ECalZRiZaTBheep6DhUoX79Bmr1O3TuwrgLfiy7dp/KlsaEJqVyMy6F5Rs+Hxl22OgpjB7cA/9X4ZSyl3LvpYLARAtWrv26lc38KDIyEktJ7qA9loIFYSFhtGjZAtMs3y+At2/fYiqxRBRFDrzchkayJpWktUnNTOXqJj/WWHkzdPSn7UxLS6NPp4FIA20oIqvOh7vxjDwzlQmLh9CkWZNvfn35Ve3atTlf5yzrrhylvLYL8coP3FWEsGD98nwH/PhWKkw7kFuiKK4RBEErK5CPCLwQRdGroO3Kj/5xHnbrydgrF3h9NJhqztoEvk3jWriEms07M+nAHRoXMyAmVcnJF0n8vDD3ZNwf+piH2oKYm4cWpnx4GsmMRSuZNnowjZ11sdKXcellAnquFfPm4UU/llx8RBUbI14mpHLjnZxlG7Z89nqGjZrC6CHdefQyktKOmtwNziAwzpKV3t+Hh/Zauf067WXGhIe+onkePLSUmSGKIpv996KZLKGeQVVShBR8f7uGt5U3P436PA/7dxmA2Ssr3HWrkPAsnknnpjB8/tCC52EDX7Ze3E9pjWIkKD/gL7xg0calhTz8d2gtsEIUxf2CINxDlZLFC2hegDaNRpViaiSqrcANUO3uKNT/gERRZN26dcybN4/o6GjKli1LREQofpcmIQgCE8btYP7CLvzYtx4AbdtV4tDBmzRpZs/JMwPo3mU1x088BCRoaEC37jXxWXuegIA3FHEpgpGxNnPndkaelk7dWr9gaKjDmzfxVK/uSp++denarQYzpx9AR8cQExMT9PS18fLuy/SZ7Tmw/xbr1p5n65bLzJ55gFm/dKBX79oMGrCBjEwl9eqVJCIihhMn7mNt5cDBg0cxNDSkTBlVINBpM9rjUX8uK5af4udpbUlISMHfPy475crduy85eeIBy5YWBhj6GhXUCug/KplMhqfndm7dusWTJ3cp7uLMTwMbI5PJePbsGTeuXcLI1RSfoc3ylZdOS0uLDJk2CalpGOlokapI5/eXkdwJfUOR6g1xc3Nj455T+J49w5v3b+nVthZlypTJ9cMsk8lYuX4jd+7c4dG9u7g5ODK0USO0tLTyPO8fIe8FQWClz07u3r3Dq5eBVO9chvG1a3+yo/h3VLx4cV5lvkIURTX7Q3lFw0q5+8cODg684w1hySGkJctppa3yofpAIh0s+nBs6x669u6i1kn7WMeOHkcz0IoKWfntjGUWWGQ6sHT2aho1afTFawwICOD58+c4ODhQvnz5r+oMpaWlce3aNeRyOdWrV1ezURAEZi2Yw9OnT7l+6Sr2FmaMa9YUfX39z7T4vVQYdOPPEgShASofo1eo+qQugiD0FUXxfMFa9u+TTCbD02erioeP7uFS2YmBjT7i4dVLGJmYsn5O/nmYKdMmMVWOoY42qenp3HoZwd3w1xSp3gA3Nzc27T+B79kzvHv3lj69P8NDnywe3r9LMXsHhjRqnD8ertvF3bt3CH0ZSPUfvi8PVyZH5eLhs9QoOlbolKu+g4MDr9PfEpzwCnmSnB4Wqmw6iUIS/ey6sGPHMbr0+jQPjx89jskrC+qYqgJbmGmbY5tpz8o5qwqehwt/yeLhNYqZmzK1+dxCHv57ZCOK4v6svwVRFIMFQTD/7BHfWaIo3gZQ7cAV/5uJD/9HFRAQQLfuXbl/72H2d/HOnXvo68tQKDJQKDK4fi2Q02dV8fXi45P5efJuXFwsmTxFtVOljLsDR4/eBzTIzFRy/14ok39uQ/cuqxk2bBLaWlLqNSiJm5stgwY3JCTkHe/fJ/FDu+WMH7cTiUSgabNymJimERAQjJenEg+P0lhbG+PgaE54WAwTx+/kzLkplC/vRGZmJpMn7mb+wq507FQNUG2/bd92JWvXraVy5XKcPPGANm0rUaqUHT4bBjBi2FbGjG3B0cMiDeoOoXYdN+Ty9P9j76zDm7zaOHyfJHX3Fiq0QLHirgWGO2y4DXeG+/ABw6E4DHd3lzFk2IDhpWiBFmpQtyTn+yOlUFpKcfjW+7pykffk2PuW/HLseR5On/Jj9Zp1yR7AM8kY/4kJKJB0hKsUpUql9LyYJ0+e9z7OJISgU58BjJk8lkpZrdly4QrFnCwQWsn5I/vZU7AQtevVp0HDRu+sS6FQUKJECUqUSD+U359HjzJ3/FjKOuhW0xdN/I1eI0fTodP72S+9L3Z2dpSrV44d23dS0cIbA6UB556fQ5tDm+pZAhgbG9Os80/MH7OYQpRFSkmsNgaNfiJWlla4RmXn6tWreHt7p9neycNncFXlS5FmoDTEIM6CwMDAtwZpV6vVDOg+gGcXn+GscSVYFYTWRcPcFXMxMzNLs8zrXLlyheHdBpMLVwzRZ2GiD636tKNxiyYp8uXNmzc5XMNXI9PmKS2mAZWllPcAhBDZgU3oPMxm8gafXA/7DmDs7+Oo6KTTw6IO1qDVcu7wAZ0e1v/0ejhv4mi8XcyREpZMDqfH8LFfRA9L1KzI8sPHqO9aDEOVPsceXyXK0eCtevhjhyYsnbCQ8vpFkVJLtDqWBKWaLJZW5Ex0S1cP/z5yhlyGKfXGUGmIWYz5O/VwUM/+hF4NILvCib0ylDhHgc+y+RnWw197DCKPIiuGQo9FcbNp+Ut7GjfP1MPvhBRjOyFECSDmLXm/CEKI0ujiHZsCrkmnVbpIKT/vlzaTz4qvry/ly5chPDwi6Xv48rsoiItLZMyoraxd3wNDQz3CwqJYt/Y0v43bTlxcIp26vPIYm7+ACyo9BepECQj27b1Mox+LU6duYYRCUKhwNs6eUeGEkwAAIABJREFUuUOuXFkwNNQnb96sXL3iT0x0AvkLuHL0z6GYmOicK0+ZvJsZ0w5QrswESpRwZfWqkwwbUZ/p0/ZhbW1C08az2bf3EsbGhvz406vfGqVSQe8+VRk7aiPjxk2ibdsWhIREUqasJ6dP+2FoqIcQAmsrY+7cn8HBA1e5cP4uSFsaNkgZfjCTd/Ppl4i/Ed4Miv0xqNVqXrx4gVb7ynyi0g8/0HXcFOaeukzfcrlpUiIvfepUZH6D0qyePZWgoKDkvDExMcTEfLj2R0REMHf8WCZXLEXLQvloVdiLKZVK4TN2dIoA6p+Lgb8OpNHoRhy1PsY2/e3k6+LF/JXz37r63q7LzzTsUYdneo+IUIZhaKsim4cbQiGIUDxP9pCYFllcHHmRmDLMgZSSGBmOhYXFW0rB2lVr0VzQ0sy6OeXsytLQqiHujzyYMWHGO+9Po9Hwa69htLOuT50slaiSpSw9XVqyduaKDAVj/xpIKTL8ehdCiBpCCF8hxB0hRKoQIEKI+kKIK0KIy0KIC0KIcknphkKIc0KIf4UQ14UQY14rM+61MgeFEFnerPcTo3w5+QSQUt4FlOnk/0/xJfSw2/jJzDt9mV9KetG4qBe9a/7AnNrlWT1r2ifXw3kTRzO7TkHalszFz6VyMatuIeb+NvKL6GH/4YOp0r8Va+OvMDfkJFkal8Zn6cK36uHPndpRs2N9HhJIqAxHZWWAm0c2FApBmAxPVw8dXRwJTUith5HaiHT1cN2qtehfi6OTawMqO5eklUstvMKyMnPSu72OazQaRvUeQheHWjRwrUANl9L092jM+pnLMvWQ70YP1wshCiS910MX/qTTZ27zXcwEqgOhAFLKf4EKX7VHmXw0EyeOo2270qhUSt50aqxWS44euUnRQqPImdOR5k19mD/3MP9c/o2EBDW7dl5Eq9Vy+fJDevVYgZubLQYGCkASF6emW9elLJh/BH09Ff0G1GLYkA3s3XsZrVbL4kVHKVNqNPb2Zvw6qkHy5BOgb7+aSClp364PJ04EMHR4fTp1rkx8XCKVvMcTG5tA9hyOaLUy1W9jYqIGpVJJ9erV2bRpO7t2PKZhvXncuqEhPDyBs2fvAGBpacJPjUtw7dpT6jf48XM/5v9L/m8noPfv+DJj8njUavUH1yGlZPnixTSrXo0BzZrQtEY1dm7blvy5RqOhfrG8FMqXDwfHLKhUehjoqajpYcvRw4cIDAykd8c2dP2xOl1/rM4vndulGIhllFOnTlHOwRpTg1dOk0wNDCjnaMvp06c/+P4yihCCOvXqsHzLctbvXU/7zu3feizuJX3790WdOwpjF0PsHOxRKJXcibyFwlmT7g5LszaN8TM6QbQ6HND9Da7HnKJ45ULpHu/as2EPZS1TuvsvZFmIv4/+/c77u379Olk0NlgbvvLqqKdQUdowPwf37H9n+a+CFBl/pYMQQskr26C8QHMhxJtbGkeAglLKQkB7YElSejy6XceCQCGghhDi5TbQFCllgaQyu4GRn+K20+G8EGKZEKJy0msFcP4zt/ndcN/vNjMmTfgketi0WjX6NWlKk+rVU+lhvSJeSXrohEqlwkClorqbA0cPHyYwMJBeHdrSpWFNujSsyS+dPlwPK7qYY2r4Sg/NDPXxdrX4cnpYty6LN6xi9c5N/NypQ4b0MNItAQMnY+wd7FAqFdx47ofaQaSrh01aN+acOE1Ewis9PBd+msIV09fDvZt3Udkx5S5yCXsvzh579/O5fv06ztIaa6NXE1w9pYpypnkz9VDHN6+HUsrxUsorSe+9pJQ/SClvf842M9ivR28kvZcDtUy+Pc6ePUOTpqVIssZO9Xn+/IWYN28FzZp14/695wwZVo8F846g1UqCnkXQqMEM+vReyagxjbhxawoLFrWngncuPHM54urixqFDx4iJicfLy4WFSzoycvgm9JVt6PvLKvYdHIyNjSlmZrrJZ1xcAuvWnmbs6K1otbpTejbWluQv4IqlpQle+Z3JlcuJe/eCCA6OwMLSiBXLTyT39eoVf0YM20S9+roJZfny5dm5cx+3b99n5869LF68jPp1ZvH4cRg+sw/gXX4SiQmmdOzQ8Us86v87/m8noO62Rpg+Po7P9EkfXMfGtWvx272dWZXLMKFiaaaUK8HeBXM5eeJEuuWE0A3GBnRrT6tsahY2K8zCZoVp6hxL/27tU+wcZJS0fjYFn3Zn41NiaGjInNWzuZz1L9ZHLWZt9AKCCt3DZ/msdO2QXF1dGb9gOOetNnBEs4S9Wh9c6qkYPXF4uu29aZP1vqRdVHyzz1dqM/56ByWAO1LKe1LKBGA9kMJds5QySr56ECYk/cpIHS+3nPSSXi8/i3itiuQyn5FuwD9A96TXP0lpmQBulibo+57BZ+rkD65j49q1+O7YybRy3owrW55JJUqzZ968jOth1w60cBTMa1CCeQ1K8JOdhv5dOvxn9HDmCh+OmJ1nXuBqfAJWcjt7IDOXzn6nHo6aO4I9BltYF7mcZRHzMKtmwK8TRqTfoJRv0bQMkkZZkamHura+Dz38FnkkhCgDSCGEvhBiAHDza3cqk48ja9as3L8XTM9e1TA2VvFqIqrFyMiAsWPH4u3tzaBBg8iZMyfmFsb4zD4ACGJi4tm39wp/n/ajVetyKJUKWrcpz9E/h3Px8m/4+t6hVKlS2NvbU7H8REKCIxk5phFWViYULepOuXK5iIyMY87sgwQEPKdooeEsX/YXKj0lOT0daNKkEblze7Fvry6sYpEi7piZG/L4USjZszuwbUc/Ro/cTOWKv+GSpSc/VPqN+PgExo8bw9x5qX0Y1q9fnzNnzqNQGON7y4g+v4ziwIEjGBoapsqbybv5v7YBbVM+G22X7iOhzyD09fXfXeANtq9ZzYRShdBTKomIiOBFaBi1rc2ZMHQImw8eomjRokwPjuF5dCxWJrqYQPGJavbdC6F5HSsKWGrJ72KLWqMmLCQE84RwDJ7eoXyxotSuU4fWnTrj4uLy1vb9/f1ZuXAh1/+9zCO/21T3cMPaXGe/ExWfwMlnoXQo+/mDfH8o7u7urNq+goiICFQq1TtdVGu1WrZt2cqeDTuwsdKnQosfaNayeYZcW9duWpvTc05R2faVY6QrL65QsnLJd5bNly8fTxQhPI8Lx8pQt+qv1qo5E3uFmXW+vTmMlAKt9r3WjmyFEBdeu14kpVyU9D4r8Pqq9GMg1UMTQjREd4zLHqj9WroS3WQvBzBXSnn2tc9+A9oA4eg8Hn42pJSJwJykVyZp0KJILrrs2E+PfgM+TA9Xr2FskWLoKZVERkbwPDSMGuZW/DZkKFsOHdTpYVgUz2NisTJO0kO1mgMPg2huaYmXKXg52yXroVlcBHpPfClXVKeHbTpnQA8XLeDGv5d5fPcWtfJkxcZctwsYFZfAn48iaPON6+GKraveWw/3bdqGpZU+5ZpVybAe1vypLseW/U0t5/LJaf8EX6e4d2o71TfJly8fjwgjLC4Ca0OdEyq1Vs3J6OtMr5N+fNCvQaYefjd0BWahe8aPgYNAj6/ao0w+ml69+tG/f3c2b+uJqakhU6fsISIiFjNzM9auWUulSpW4ePEiQ4YM4ezZi0RGBhIfr0a3/6VFo9FgZKTPkyfPyZ37VWzNJ0+eY2VljhCCLFmy8vvvPqxYsYTHjx+j0lNhbmFEdHQcT56EYW1jSrnSo2ncpBS/T2kOwMhRjRg+bBOHD57h1q2bmJkZcOnSA27eeEJOTyc8PZ3w8nLhhu8Ucnr0o32HiowZ9xNKpQI/v6dUqvArefPkpVKllF/V7Nmz8+jRI+bPW/gFn/L/J//XE1CFQmBlrCI6OvqDBlya+DhM9PUJCQ4m5vkLbAyNsbG2IervS/T4uR2L1qxm4LhJDBwxiHJOZhgqFfz5+AXNu/VFrVbjbKpEo9Xgf/8elnoCR0MFRZ1MeHglGPU/f9P/1EmmLF2Om5tbqrbv37/P4I7t+TmXB21LFWKjHrRauZEmxQujr6/PyWdh9Ph15FfyOvh+ZMSTJsCoQSOIPfeEZo4lEErB0dWnGHX5GlPmvtuOs0XrFvxz+h82XF6Ps9qFYL1g1FkTmTv83ZE4lEolY2dPYET3IeTGDQOtHlc1d2jWuw2urq4Z6vuX5j2dboRIKdMK8wFpbyalWp2XUm4DtgkhKqBzoV8lKV0DFEqKu7lNCOElpbyW9NlwYLgQYijQk88YcFwIcY807kVK6Z5G9v8kCoXAykj/g/VQHR+H8Wt6aG1ojI2VLVEXLtCjbTsWrV3NwPETGTpsMGXsLTBQKvgrMIwWPX5BrVaT1VgPjUaD//37WCgV2OspKWxvzsMbwcSfO0e/U6eYumzZW/VwSOd2dMzvQqdKeVlvoqHJvB00L5sffX0D/nwUTveho/+v9HD04OGoLz2krUtBFEJwYNMxRv17lSlzZr6zbPPWLRh45gJLrm0nu8KJAMKIsZf4DJ3/zrJKpZIxsybya49B5FM6Y4gel+Mf0LRHph6+au/b1sNvESllCNDynRkz+a6oX78+AYFPqF5lJKamBoCS9h3aMmvmHI4cOUKrVq3YsGFjkvmH5Pr1x6hUChITQeemQZKYqKF716Xs3D0AU1NDYmLi6d93PR07vTJbrlu3LvXq1WP16tVs2z6fY0f/5fbtpyiVSnbs6kcO934MGFQbjUZnH7pu7WliouO57feUP5Z1ZuqU3Wi1urbs7MzZs/sS8fGJrFp5Eo1Gmzz5BMiZ05HhI+oxc+bUVBPQTD4d75yACiEMgTpAeSALEAtcA/ZIKa9/3u59HC+i44nQ6GNpafnuzGlg7+LKvaAQ5PPnOJuaI4CzAYGUyZYTkzgtR48coXqNGqzYsY+TJ0+SkJDAvLJlsbKy4t69e0xdEUP5bKFY6CswUuq+ahceRTKhekEmHveja4UKLJ3jw5gpU1O1vXj2LLrm88QriyMArUsWo4irC9Ou+dG9f3faly+fypuhlJKLFy/y78V/yOLsQuUffviggebX4P79+zz4+wa9sjdITmvoVoGFV3Zz8+bNVHZSCQkJHDt6jMcP/clfuCDFixdn9pLZ3Lx5E19fX1xcXChSpEi6x9uklFy+fJmL5y7imNWRVXvWceHCBWJjY+lTehS2tl/Va326fEKvj4+B17ednIGAt7cr/xJCZBdC2CYNKF6mvxBC/AnUQKcPr7MW2MPnHXC9PqA0AZoCRm/J+8F813oYE0ekVH6cHoYEI8N0eghwPjCQMm6eGMfB0cNHqF6zBit27U3Ww/mv6eGUpZGUcwnFXKXASCFQAhefhDPauzjTzl2jc7mK/DFnDmOnTEnV9hKfmfQs7E5+Z13s0bZlClMsW1YmXnhA937d+SMdPbxy6QJOzq5Urvx96aH/uasMyPcqbGOTHKXxuX4oHT08ypOHj/BK0sOZi3yS9bDWe+jhpfP/4JjFiZW7NyTrYY/SpTP1MM12v009FEKkG19TSrnic7X9NoQQ7kAvIBuvjT2llPW+dF8y+bR069qdDu078vDhQ+zs7DAwMMDb25ubN28RFRVJkoEEoMDS0piixdw5fOg6cXE6E2C1WsuF8/dwd+1LgYLuXL50DxsbO+7e2cXWLZvp0qUHtWrVpGmzxrRv14mzZ/yYPrMVNapOwsPDjnFjtqNSKdBotHRsv4j794MZOrw+SqWC2jUm0+jH4vTuuYKjx4dTqvgoTvx1i8o/5KVWjclopcTZ2Tp58vkSN3c7Hj7MdCPxOUl3AiqEGA3UBf4EzgJBgCHgCUxKGoz1f2ns/i0RE69m4EY/ugyc8MG2gT2HDGVgu7bUszbHSKHHlaAQttx5yIiqDQmMeMG1CxepXqMGxsbGVKuWMii4h4cHLoXKMWnvVjoVc0KtTmTblWdks7Qgh605diZ6uFhZ4Hvhappt3715k1+8U578yefkgMkNP2rVqpUqf2JiIgN7dMcw6AlF7Sy5fjSWZbNnMuOPZWTJ8rkd7n08N27cIJe+U6p0T6UjN27cSDHgevbsGT1adSZHoi1OSmuWrz7GMncTZi2Zm+EwEhqNhr5d+xJ++Tk5yM5VLrPIeCFzVs9N9xjgN8OnsyA6D+RMGhw8AZoBLV7PIITIAdyVUkohRBFAHwgVQtgBiUmDLSN0uwC/J5XJKaX0S6qiHnDrk/U4DaSUYa9dhgFThRB/odud+CR813qYkMivhy7TddjYD9bDXkOHMODnn6ltboWhUsXV4GB23L/P8CqNCIwM59o/F6le8+166FqkLJMP7aB9QRc0iYlsvxGAq4UlOawtsDUywMXCghVX/k2z7bu3buJVvXCKtHxZ7DFVPnirHg7q1RWTiAeUcjbF72w8K+ZOZ9rCFd+NHuY1ckiVnsfALk097N22A/mU5rgYmrN+yz5WZLVmxsJ576WHA7r3IfbmM7wMnflbfYYl0+cxa8WCTD38DvUQKPra+2bobFlfv/7iE1BgO7owLLuA9zf8zuSbRl9fn5w5cwIwefJkrl27TmxsbNKnkpdO6Q0M9Fizrgfduy1j88ZzAJiaGpEtWw62bdvJ1q1bOXd2KA8ePMLAQMHQYfUoWMiVew+nM27MDgYM6EOpUmXZvOkflizrzIH9/7Jl0zmUSgV9eq3k9Gk/fO9M4+jR6/w+cSempoacOXOHkJBITp24jYGBCq/8Lmze2ocF848wdcoeXjyPxs/vKTlzOibfz8rlJ8iTJ/+XfIT/Od61A3peSjn6LZ9NF0LYA9/kmZwIrSHDpi/HysqKhw8f4uLi8t6ByXPnzs34BYvo0aQpZ59F4m7jwJjqP2JlbMKZJw/I6lEm3fJDRo2je6cApv51ChOhoaanM97uOtfPwdEJvIiNxd4p7cGQrb0DAeERZLV85YnweUwshqZpx3DbumkTbpHBtC736nenaMAzpoweyYxFS9Is8zUJCgoiLi4OFxeXpDP+WdirDU+VL1CGU+GNAePvv46njn5hcjvqTleWxIu9D0+xce16Wv3cJkPt79y2E/XFBJo4vHKf7R/5iLEDx7B4/Zd5XhqNhkePHmFubv7WQPRp8gnj3kkp1UKInsABdL8QS6WU14UQXZM+XwD8CLQRQiSi2/FrmjT4cgJWJNk9KYCNUsrdSVVPEkLkQjfQeIjO/uezI4QwA7RSymhgjhBCIWUGXI9kjO9WD6OEPiPmLPloPfxt4UK6N27GhdBw3K0dGFW9MVZGJpwJeEBW9/TtrYeMHkv3x0+Y/vcZTJBUc3elvGtWtFISEhPHi7g47J1SL0KBTg+fvIjE2erV8dXn0bEYmrxNDzeSQ/uEDjVfOTAt6R/C1LEjmL5g6Xvd95cgLT08qI5Mle+JOpKSb+jhlNHjaGrjSX4H3USxHLnYcvsfNq5dR6uf090IS2bn9p3o3YqgZfYayWn5XwQwfsgoFq75Ms8rUw8/HVLKZGNdIUS5N68/Z9vpECelnP2V2s7kC7Jq1SpiY+NIvTKkJSgonPPn77FiZTfmL2jP8+fRtG29mGbNuuLi4sLSpUuJjY0HoEKFvAwb0YATfyqwszNnxqwWFC4wkjZtRnHp8kUG9F1OdHQMVavVZM/uPRw/fhPvinmoVmUifrefUr9hUZ49DadzhyXkzpOF2bMP0KNXNfbtuYyenopevatTp25hihQcRtXKExgyrB6ubrasXX2KA/uvcOvW1i/+7P5LpDsBlVLuecfnQeh2Ab457Owd8Jk1hoSYu5iaKgh4pk+/gZMpXjz9AOdvUrBgQbxr1cLpURC1c+ZDIRT4hTzj+IsgltWtm25ZIQQjxoxjQLsWdCjqiJk6hniNhhX/3KeAswuL/r1J9wlpe6Vs3a0bc0YOZ0iZ4lgYGRIdn8Cc85do0btfmvmP7dlF39zZU6R5ZXFg3t6/0Gh0cY2+BYKCghjUfTDh96MwEAbEmkYzdtZoChUqRLQtXAi6SVG73ABcCblDkFkcJUu+GthKKfG7cosmOVulqLeCY2FWbt+X4QnogW0HKGOR8v+Cq5kLe+8cIC4u7rN7Nfvz2DFmj/0dB4Up4YkxOOTJuKmi5JMeOUNKuRfY+0bagtfe/07SSv4bea4Ahd9MT/rsiwbGEkK4AWsAd8BKCHEa6PwJJ5/ftx46OOAzeRwJoQ8xNVASEK2g38iJH6aHtWvh+DCEWjle6eFfEU9ZXi8Dejh2HP3btuTnvG6YxscSr1Gz+tod8ju78se1a/T4PW2v5a26dGfWmKGMqFgIC2NDouITmHn6Gs27D0gz//H9OxlWyjlFWgFXW6b/dfGb08MhPQcR4x+OvtKAKMNYRk0fS6FChYiwVHIu0I/ijjkAuPzsHgHG6lR6ePfqDbqUSPnsq7nlxWfnngxPQA9v30sdxwIp0jwss7De9+QX1kMTwtWxOOTO1MNPSLwQwkNKeS9pl/ZrnUOfJYQYhc75UPzLRCnlxa/Un0w+E3p6yQ6gkzE2VlG0mDuFC7vRsN50GjQshmcuJ7ZsukiWLNlp93M71Go1V69eBRTo6Sn4oapXcvnVq04ysP9awsKi+Omnn+jSpQs3b/qhp6fHvHnz0GoDqFXbi4H91xAREccN3ylYWBiRPVtf+vavyfy5h4mOjqdW7UIsXniUPXsuUbt2Ydzd7Zk8tQW/9FrJxN92otFoSUiAbdt2fRenZb5nMuSESAhRDBgOuCWVSfJ4LwukW/ArEvDEn2a1rCheWPcfKOx5An1H92L2vN3pBv5Oi1G/T8Jn6jT6HTiIQkKW7B5MW7o4lc1RWmTNmpVh0+bgM3Ec/n4PCAoOxdjEHDcPBzoN6E/x4sXTLFeqdGliBg9j9KyZqKOjUBga0bJLD6rXrJlmfj19feITU8b4k1Ki+cjwJJ8SKSW92/Wh8LPSZDPTTZbDE14wpNMwNhxex6yl85kyZiJ7T69BIMhboiBzRqcO8C4BrdSiEK/SEzSJ74zF9zr6+vokahNS9U+D5r13ht6XBw8eMHfERAbmroGpvs5M8WzA+4Vo+3TTqv8bFgAzpZSbhRAXgS68iuf3Sfku9fCxP82aelC8uu44ZlhUHH1H9mX2yu3vrYejf5+Iz9RpDNi/HwWQNbs7099DD4fP8GH2hPH43/YlKDgEY1Nz3Dys6TykX/p6OGAEw2ZPRxMTjTAwpGXnXunqYVxiyhCDUko0Wr4pPezboTfesUXIkVXneOl5XDgjug5hzYENzPxjAVPHTmD737pYq3mLF8Jn1KJU+qQltR7Gq9XoG76HHhroEx+b1u+H9ovp4aA81V7TQ793lEpJph6my0TgtBDiPpAdGPaV+pEfaA1U5tURXJl0ncn/CVJK6tSpw7Vr10lM1I2xlEoFtWoXZN2GXgghGDCoDqNGbmb61P2sWLGG2rVro1Ao2LNnT3Kop8REDfv2XqZf/1pERMTSrctSYmM1gM7W848/lqJWq5k/fz7Pgp7h7mHNjGl7CAmJonSZnLi42BAUFE6u3E5MnbyHdu0rsHTpX6xbe5pNW/vQuNFMcuR0xNzciKNHrjP+t98oVrQ4BgYGlCxZ8ptZpPx/JqNecNcAA4GrfCdn94VIoHhhq+Rrayt9fqylYu+ebbT9uXOG6zl/7hzL5swl9OkzPPLkolOfPuTOnfu9+pI/f34Wrd2YHKsyozErK1epQuUqVTKUv07T5qybP5N+5Yol5z1+9yH5S5ZKNYBISEhg2aIlHNu9DykllerUpH2XTh/koCMhIYGlC5dyaNtBtFJSpV4VOnbvmOZk0NfXF8VTveTJJ4CFviW5ovOzf89+mjRvwvjpk5IFKK17FkJQolIZzly4RpmkFXspJXsDz1B/SPMM97tRm0b80X8xTYx+TB64XXtxnXwl8n02RyWHDx1m5dwV3Lh6hcb2+dHnlcCVzOL5HjUJZJrOGv/TOEkpNye9F1LKu0KIz+U15fvTQ20ixXO8mmhamxryYwEL9u7aTtv2ndIpmZLz586xdPY8Qp4F4ZE7F537/fJBerh43YbPqoe1fmrByhVTGFozb3LeIzee4FW8TJp6uHzJIo7v24OU4F2zFu06dflgPVy+aCmHdu4HqaVynWq079rprXpoEKYkh+Mrr79WhhYUVuRm/979NGnWhHHTfn+nHhb3Lstft25T0U33d5BSsuXeZer1bpfhftdv2Zi1I+bQ0bRGsh5eDPIjV1Gvz6qHq+Yt58a1KzS2zY/Ba8ORkllyvkdNmXqYHlLKnUn28DmAh1LK4K/UlYaAR1J81Uy+c4KCgnj06BGenp7Ji4/+/v40btyQp0+fYGysT3i47k9tYmJIz97VkzUsSxYrFi3uyJFDA8iVKxcKhYKEhAQ6dWpP46al2L3zErGxiZz5+w5DBq/D3a06sbEvQ7cACGJj41i6dCn79+/mwYPHmFsYoVAoMDRU8TwsGq1WS+0ak/GulJdJvzdj0MB11KxZkO1bLxARHsuMWa3YueMiWzafY86c+XTs2PErPMX/Nhld2gyWUu6UUt6XUj58+fqsPftIVGksXthZq3jxPOPae+rECWYPGkprSyemlKxILWHMr126cfv2++1WveTll+99V+Azkr9a9eo4lKlIvwOnWPHPNcYdP8+xOEH/4b+myCelpH+3XkTsO8+w7JUYnqMyUQf+oV/XHh8UZLxv577cWXGblqrmtNFvycO19+ndoXeadYWHh2MiU++SmGJGaPArHzJCiHTvuf+IQfg5hbPowS52PT7J9HubcKmen9p16mS4394VvSnWvARLQpZzMOQI60M24+t8lxETf3134Q9gy4bNLB2ymFoxlcmn9MRGY4b/PX8S39i1zihSigy//iOkWEwTQpQAYj5TW9+fHipS/z+wM9PjRej76eHM/iNoaujGb/mrUTXekhEde3yjelgDmyJV6bbhXxafuMPwXTfYH2xKv6EjU+STUjKoZ3fk2aNMrZCfad75EeePMbBHtw/Sw/7dfiFw87/0sqtNb/t6hO64SZ9OPd+tzR8tAAAgAElEQVSqh+aYpEo3U5gQFhya4n7Tu+e+w4ZwyUzNtCvH2HD7H0ZfPIBDhaLUek89zN+wHJNvb2G7/ykW3dvHefNAhv/2eRy1btmwmZXDF9BQU44Cejmwk6b433+YFKrh/cnUw5QIIQyFEOOFEOeFEOfRLZjd+IqTT4B/gQ9zwZ3JN0NsbCxNmjTB1VXnWdze3oERI0ag1Wr58cf61G+Qkzv3pxD6YgHlK+QCdDr74kXKn+PERA3R0XHJcY0vXLiAo6M5K1Z2pV79IhgYKlEqFcybc4jY2ATejJCkUilwcbHC2cUEj+z2ICWurtYIIQgJjWTwoPUgBFOmtqBsuVzExSbQum15zl4YRzZ3OxYtPIqvbyA2Nmb8/fdpnj179kWeXyavyOgO6CghxBLgCCnP7n+zFrqxcRAbq8HI6NVM9PDJOOo2rppuObVazaYN6zm8fTtXLl6iSZ5COCXFbctpa09rN0/6duxE3Z9+omylivx78RKB/v4UKlmCSpUro1LpHumpUyfZtGIJYSEhFC9bgVbtO2FlZZVe06l48OABKxfNx+/GNdyy56RNl+54eqa9UyaEoM/gIQS1a8+tW7eo6+iIp6cnUVFRzJ81h+P7jxATG4O+hQkGT17QrXS95J2AOtkL4XP9GNevX8fLyytV3VJKdu3YxdaVW4iLi6dag2q0bNuSe/fuEX7jBTXtmiTnrWBbno2+m7l69SoFCqQ8kejl5cUjxX3U2kRUCr3kum+LGzSrPDRDzyQoKIhlC/4gLCgUQ0cT3KuXpHfdujg4pPYYmR5CCH4Z+Ast27Xkxo0bODg44Onp+V6D4eN/HmflvDW8eB5OhWrlaNelbZox/rRaLct9ltPJoRX6Sn08rXJz+fll6hkXIiw0FAdHB6ISYtNoIW2k5H0Dr/8XWC+EKJBkh6WH7thZxrf23o/vTw8TJbEJaoz0X0n+4Vvh1O1dJd1yarWaTes3cHDbDq5cvMSPOYriaKZzjJbd2oGmMfno074zdZv8RLlK3vz7zyUC/P0pXOoNPTx5ko3Llur0sHwFWnfo8Nn18JeBQwlq24Fbt25R/TU9XOAzm5OHDxETE4OeqRlmz5/yY11vFArdb8VPBXPj++c/6erh7p272LFmI3Fx8VSpW4PmbVpx7949Ym+HUMP91cSvqnMJ/ri/9616eFftT6JWjZ5ClVz31QQ/BlVqlqFnEhQUxIqFi3kRHIKhjSmOVSvS6QP1sNeAPjT/udVH6eGq+asJfx5O+Wrl+bnz2/Vw5dxl9HBpgoFSnzw2nlwKvkp9k4KEhYRh72ifqYcfz0wgEZ0H3y3AdcAH6PAV++QA3EqaEL+umx8dhkUIUQOYhc5x1BIpZdrG5Jl8NL169WL37j3Ex6uJj48GJDNnziQiIoInTx4ycHC/5LHloSPDKJBvMLdvP2X4kA14e+fGzMwIKSXTpuylQIECODvrbPUNDAyIiUlAT0/JmnU9uHLlIS2azuWlBAkhX1tAkqhUCmrXLcyN649ZtKQjbVrO4+HDUDQaLfp6SlavPEnNWgURQjB2zFaCgyO5fu0xNWsWpGq1/MyZfZDWbcpRomR2Dh++SZEiBTl+/CQ5cuT48g/1P0pGJ6DtgNzoBnavn93/ZgdcNrZO9B0dQMuGRlia67H3aBRaVfG32hi9ZEifX3B4GsjgPLm4p4SzT54x88+D9KtUg6eBgVjFJRL3KBDDExfoOnU6RVw9qJYzL6fPLWLDshXMWfYH+3bt5OjqufQomx3HYu4cvfk3PdoeZdHazRkOlH7v3j2GdvmZrkVd6FUtF7cCQxnTswODpsyhYMGCby1nb2+Pvb09oDsO1rVlewrGWFM/zh11YiKLbhymjL0r9+/cw9XdLclYHHIYWHL37t00B1yTx07Gb+ctqlpVxEBpwNnFF+h2uCv1WzYgqzq118qsmizcvXs31YDLxMSEDv3bsWrSCoopymCgNOCK+iK5quYgX75873wmISEhdGnSjsp6+elhW4fA6BC2Lt5GNlc3HKqlv7DwNmxtbalQocJ7l1u1dDVbp+2jjH5NzPQsuLH0Em0PdmDtzpUYGaUMPxkVFYWR2gB9pe4oWyG7AvwTdIFtT/7BK8GJB5oX7A+98V7t/1dW8jOKlHL8a+9T/yf+tHx/emjvSN/1t2hZzAZLE332XgtF61jg3XrYuw9Wj0Lok8OLu3EGXAgKxOfUPn4pV5ungYFYRKuJ8X+K8vBlOv0+k8LO2fnBw4vjp5exfulK5i5fwt5duzi0ZCEdC+XDwT0Lf924RPdWLVm8YeP762ERV3pVya3Twx4dGDT1/fSwx89t8DZW0MXOAHWikkmnLlHIzZ4Hd+/iks09WQ/zWRq/VQ+n/jaJgMOXaOlWDCMzfY5t+puex/6iTtNGuCvtU+V3V9q/VQ/b9u3A4mmr8DYphoHSgHPRV/Go6JlhPezeog0NbDyonassT8Kfs3Llxq+ih6uXrmbnzL1UNKmGuZ4FV1dfpt3B9qzeseoteqiPQZIeFnHMz7nAi2x9fJF88U480L5gX/DN92o/Uw9TUealTboQQiOlXCuE+OUr9+mzbKcneR2eC1RFF8f1vBBip5Ty/X5UM3kncXFxrFmzhri4RF7tSAqio2OYO3cuXvmdUau1zJq5hz+WHEet1lC4sBt2dm4kJCSSzaUPP1TJz907wWi1+uzevR+A4OBgHj16RFy8lo0bztC0WWl+n7iLWrULMXR4PVYtj8bISI/Y2MSk77oGpVLF2tWnWL+pF1myWBEREYutnTlz5/1My+ZzMTbR58Rft4iIiGHWjH0ATJ60kzJlczJ08HqmTGtBq9Y6h9A/NS7JxAk7GTlyGGvXbvzyD/Y/SkaXDQtKKYtJKdtKKdslvdp/1p59JEZGRgwfvZ5rD6uz/+/CVKg+mXG/zU53RffGjRtE+d7ix1w5MdXXx9LYiDZeeYiIDsfvaSDxkVHEqNV42juQXc8Qn7I1eBD0FE9bB37OX5y8cZINa9exbslcxtbKj6utOfoqJTXyu1HdRZ8dWze/te03WeIzgz6l3Cnm7oRKqcDL2Y6h3rlZNCN1kPa3cWD/fjyiTMhvkAUbpQl2hhZUtsvPpVB/LBSGPHv66sjB/YQI3NzcUtURHBzM6V2naOBQF0sDS4xURlS0L4/qgSAiIoJAZepjC89Uz9KsC6Bx85+YsnEi+vU1RFcKpce8ToydPDpDK+2r/1iBtyovxRzyoKdU4WruSKdsdVkwZc4HHZf7UOLj41k9bz01TVtibWCHnkKfghYlsQtwZ/fO1I5STU1NiVHEkqhNBEAplHTI155EfVv26z0mprwr09e9T+gXAfI9Xpl8ar5PPZy5kmvGpdgfnp0K7UYzbvLMd+ph+A0/GrjnwVTPAEsjE1rkKEBkVDh3ngUQFxlFrEZNTlsn3BUmTC1ah4fBgeSwcaRV7lJ4RirZsGYdaxfMZ1j5krhYWaKvUlLFMweVrM3YvmVLhvu/xGcGfUp4UCzb63qY57308OD+/RTQk5S0t8TB0ABHUxPq5crGmSdBWOvrEfT0aXJe38i4t+rh+f3H+TlXJWyMzDHWM6S2RzHMnsUTERHBQ01IqjKPtKFv1cOfmjXmt9VTiKmkT0DRaNpP786oSRmL1bpm6XJqW2ejlGsO9JUq3K3t6FeoIounz/rierhmwToaWjXD1tAOfaU+Ra1KkCXIjT1v0cNoEUeiVnfcVikUdC3cFq2hDfuUT4gq68a0TD38WFLcqBDCgoxvOHxykiaJv0opj7/5+gTVlwDuSCnvJdmXrgfqf4J6M3mDqKiot2iLQKvVcutmAFV/mMDY0du54xfMg/th7Nh+kZs3/bCyssPKyo6wUEOGDBnP5cvXcHV1Zfz48bi4uNC27c88expCx/aLKV92LNu2XmD4rw2wsjLF1NSQDp0qUr1GfqxtjHB0skBKiaGRHkqlAk9PJ/LmcyYoKIJfeq2kbDlPypb1JOx5NI0bzUKrlZQslYMFizrQstlczpy5Q9NmpVLcQZu25Th06PCXeZCZABmfgJ4RQuR9d7Zvh2cB95g5ZSTtOvRi+K9T8fb2TtebX1hYGL906Yq7OpEXAYE8uOOHgZERz2KiKWBjzfXH/kTEx7HwxiV+LFgUTWIi5oaG5Ley41awbuBSyS0Hh3fuIquZPgZ6KbW+mKsN1y+ey3D/79++Rb6sKX2ouNlaEPr0SYbruHbhMnnMsxIZEcnugPPMu7ObCG0Y4YkxjL+8h/CICDRaLUceXCPWziTNnYTbt2/jpnJJNSDKJrIR/SIagxwG/B1yBo1Wg0ZqOBt6Dq0bFC6cpjd6AHLlysWQ0YMZO2U0ZcqUyfAxr6vn/yWPVUr3/CZ6RogYDXFxcRmq41Pw5MkTrLT2KEVKQ2MXZQ4unf43VX6FQkHzzi3Z8mwX0YnRADyOCuCpWQgL1y2j98B+uLq+R/hICVIrMvzK5JPz/enho/vMnDiGdl16Mnzc7xnSw96duuEcKwl7/JT7t+9gYGREUFwUeS1tuRHwkKiEOJbeOUeDfCVQJyRirm9EPgsHbocEAlDB2ZODO3fjZGSIgSqlHhZycuD6hfMZ7v993zT00MaC0MCM6+GNSxcp5GBDVEQEa6/dZuxfZwiNieR5bBwDDpzhRaROD/dcv0O4ifVb9TCXiUMqzcpj7EhseBTC1ZTjTy6i1mrQaLWceHKJeCfVO/Vw8KghjJ489r308PrFSxRwSBlqxszAEFV84hfXQ1vsUSpS6qG7fg4unUlbD5t1bsn6x3uJStTZhT2KDOCJSRgL1y3P1MNPw30hRKGk95bAOWD01+qMlFIDxCRNhD81WYFHr10/TkrL5BORmJjIli1bWLRoUdKplTcnoRKlUoVaLTl18naSwyCBbmIqCQsL4+DBwzx48IgTJ07RpUsX7t69y6FDh5g0aRLx8WoiIiKJi4sjNjaBv0/fRqvVYmCgYs+eSyBg1YoTREbG0rhxCV48j8HK2oS69Yowa8Z+YmLiyJLFCqVCUKduYbp2r8qpk7cpWTI7rm42CAH16hel0Y8luOU3FUMDPcLColPcwbNn4VhYpDYZyOTzkdEVsXJA2yQ33vF8B2EHXOz0aVDgKZPGDWHClHnvzD960GB+MLfjxfNAbI1NkFISGBWJmY015/69yp3YJ1hqBX29q5HTzpF7zyOQSCITEzBJ8hIYHheLta0tgf7BaLUSxWuOP+4GR+Dsnj/D/bexd+Dx80hcrF99IcLSCbyeFq7Z3Xl89jQhYU8RigRGFtB5IfshS3YOP73NwAtbKawtQtmqlZj1S680Bz5Zs2YlSJvab0GQDKaQRzHadGjD/Bnz+GPfMiRQvk555g0Y/llCHbhmd+Pxv8/Ia+CRnJaoUROvUL9XCJaPxdbWlhcyJJU3zmBNIF650t7paNG2BabmpqxdtIaY0Gjcc3swa7jPB8eZyjxy9lX57vTQ2cqI2g6RTBo9jAnT57wz/6gBQyivn5XwmEBsDE2REp5FhmNua81F3wvcS3yApVrQq2xNctg6cjc0AoDIxHiM9XTfxfC4WGxsbQm875tKDx+EPcfZq0iG+2/j8BY9NM24Hjp7ePDgyA3CAgOQUs2s6sURQlDbMys7bz+mw46TFPaPplTlH5je65e36uHjuBep0p/Ev6Ckuxut2rdlwcy5TD24GSSUrVoBn34TPoseuri78+BRCAWdXk3WEjRqYtF+cT0M1aTWw2cJT8mRM+2JZIs2LTE1N2P1otXEBEfj5unBjLlzMvXwE/GGXWUNwF9KmXHD2s9DHHBVCHEISB79Syl7f2S9af3xU23TCSE6A50B7Ozs+PPPPz+y2U9HVFTUN9kfKSXR0dHcv38ftVqNgYEhw4cPR6t93fm7QKHQeTN/tTv6ylbz9Xyvc+LECaSUjBkz5o3WdfkUCli3Oo64OFeyOjuwdNkCAp48Jz5ezcSJdTA01MPJyZJnz8JZtiSS2rXaU9E7DhdXW+7eDmHgwJEUKOBKfEIi+b3qY2psyok/jQAj5s2bzYF9kmxuhgihmyTfvWvDuPETM/R3+Fb/Xt8bGZ2A1visvfhMVC5iy/JDF4mNjU1hhxIbG8vc6bM4efAoaCWehbwI8btHv3JVGLRzA2cDAijh5ISlngFH797DKHce9s+bT+dGP5HVwhIhBMZmZlwJCiQwLoYcNvaotRrW+12lzbiRnD91nEUnj9O+TE70VUruB4ez9mowswa2zHDfW3fpyYwxgxhRNT/WJkZExMYz9dh1WvYckuE66jasz8/L1hL+4hHDcldGABGJMeir9Kjl4sU/vGDL0f3p1pEtWzZs8tpy5uY5StgUQyC4G3GXB6aPqFK1Cvr6+vQfPoD+w9MOCP8pad2lHf1bdMfO2Ao7Yyvi1QlsfnSMhm2bfPZYda9jbm5OmZol+HvHQUqY/IBKoeJp7GNum15gdLNeaZYRQlC/UX3qN/o0J4MyB1wpEUK0Te9zKeWKT9jcd6mHFfNmYdXqy2nr4bTZnDhwDKmV5Cqcl2e+D+hcsC4jj6zkQtBjitplxVxlyMkHfph6eXJwwTw6NmiMk7kVQghMzMy4GhTA04Rosls7oNZq2PzgEu0nDufciRMsO3+a1oXyo69S8iDsOZvvP8bnt6kZ7rtODwcz4ofX9PDPa7TslXE9rFO/AZ1WryTGP4ApPxQEAWFx8RioVPyYz4O/XqjZcCD9I1jZsmXDLIcTx/yv4O3ihUBwI8Sf64QwLEkP+w0bSL9hAzPcrw+lZaf2DP65M45mFjiYWhCXmMjKG2ep37LZF9fD0tVLcnzPYcpZVkKlUBEQ84SrhhcZ3Cxts0MhBPUb1qd+w0w9/BykoYelkkIefUodfF/2JL0+NY8Bl9eunYGANzNJKRcBiwBy5colK1as+Bm68mH8+eeffGv9uXbtGsOGDSMmJgaNRsurA5MSpVJJliyOmJmZUbp0aYYOHcqAAQPYu3cfCQnxvJpaSEDzxrUWkCgUCqysrAgNfY5u0vl6PgAthoZK/O5N546voEx5cHMeiZmZERERsbRtV4HVa3zZsasf2VwHcPX673iX+w13D3tKl87B8eO3yJGrAe3bLsTc3JDg4Ehm+bSheYsy5AhOpOoPE3j2NJxSpfNy/pwfderUYdGipcl+AN71fL61v9e31J+Mku4EVAhhKqWMSi/EwMs8n75rnwZDfQWJiYkpBlyDevbBIxDG5KmHQggO+/3LvwGBCAS/VqvP0rN/seK6L/EaNcLBns3712Fubs7ASRMYO2IkbgbGxCQmcsn/Ps42tiy8dh6/yOc0aNuGMmXKUKpUKZYtsqDz1k0ITSJ2zm6Mmb34vTwTlixVirhBYxjpM534qHBURia06NyPqtUzPvY1NzdnxooFNPKuygttDOGx0eir9FAolRiZGmNrm7EwiVPnT2PGxOnMO7AYtJI8RfKyYMyCzxYf7m14eHgwct5EZo6bwvO7oSgNVPzUqRkt2rb6ov0AGDZ2CAtsF7J1w3y08RK3PC7M/W0m1tbWX6YDX87E63uh6Gvvm6GzA3r9+qMHXv8Pemikp0ylhwO798XZX5/Bbk1RCMGf1/7h4uOnKAopGFiuCasvH2bt3avEq9WostqwZes+zM3NGTz5N34fPhoXlQkx6gT+DbhHVht7lt7+m7vRoTRq1ypZD5cuXECfLVtAo8bexZVx8xZ8gB6O1ulhZJIednl/PZy86A+aVK1EaHwCYbHx6OupUChVGBpnXA9/nzODWb9PY+ShrSAluQp54TN3yVfRw2EzJ+MzcTLh14MR+no0at2C5m1af9F+AAxN0sPVGxejTdTi4umCz/hMPfyKvK6HJugc9JzhE+jghyKlXCGEMAJcpZS+n7Dq80BOIYQ78ASd3rf4hPX/pwgICMDPz4/evXsnLey8nHxqkvNoNJLQ0FD8/f2T0zZt2sTs2bMZNGhQ0oRV8OqL+fJfTVJdOpvR0NCX4aYUr+V7tZhUuEg2nJysuOMLhw5dJTIyjgOHh9Ct81KWLztOoUJu5MoxgJjoeBwdLWjWogx/LD7GhElNmT/vML/0XIHPvLZ06fgHJiYGDB+6kZ7dl6PRaNHTUzJ48AgKFCjA3DleuLunNO/K5PPzrh3QHUKIy8AO4B8pZTSAEMIDqAQ0ARYDGfeu8wXxfxqN1LdL4Qr+zp07xN17RtU81ZLTqrgXZP21Izx49hR3Ryf6VtQNahZeOkPlvj2Sy5csVYoNB/Zx+/ZtDA0NyZYtG8HBwQQFBeHh4ZEcz0ihUNCha3c6dO3+Uf33rlQJ70qVPqoONzc32nTvQtBpX7xdc5OQkIC+nh7/Bj3Cq3hKuyRfX182r95EeOgLvGtVonqN6qhUKoyMjBg2djiM/aiuZIhz586xZ+M2tFotNX6sl8omqnCRwqzYtvbzd+QdqFQqevbrQc9+Pb5421Jmrvi/yevHuIQQ5d68/kTNfN96GBIJJjap9DD6TgiV3Osmp1V0KcrmWwd4EBRANvss9CilO823/NZf1BjaKYUebjy4J0N62LFbdzp2+zb0sFWXbjy8fZ7qebORkJCAnp4e/zx6St7CRVPk9fX1ZevaDUQ8f075aj9QrUaNZD0cMnoEjB7xUX3JCOfOnWPf5q1otVqqNayfhh4WYemm9enU8GXI1MNvizePtSbZXq76St152Ye6wFRAH3BPslEd+7FhWKSUaiFET+AAujAsS6WU1z+6w/9BQkJCKFy4MIMGDUr6Tr38Xr2chL66ftPOXKVS0a9fP+rVq0ezZs24du06QoCenh7x8YlJO6MiqR71a+81pHQm/+q7/PhxWPKx3oP7r2BgoOLQwWuoVEpmzmrDHJ8DSAmmpgZs2niWEb82YN3a09y4/oSGjYqza+dFgoMiqFe/CLPntGX1qpOc+duPbO4OPHn8HKEQ1K1bl0y+DulOQKWUPwghagFdgLJCCCt0/3N80R2laCulfJpeHV+LsIhEBi99ztipy1Kk+/v742qQMv6cEIKqHsUYdfYYTXIXwM7ImNNBgeh7ulOpcuUUeZVKJXny5Em+ft3N/7dK+26d6f53e576XSC3uT33okL5V/2cuXNePZs9u/bwx9gFVDYphbOeMwfHb2fv5t34LJ37xY5zzZk+i8tbj1HNvgBCCFYNmcbf1U8wYETGj9n9N/jvBFT/QOKFEB5SyntCCDt0A56P5rvWw+h4ft33gHGzU3oX9ff3x1mVcpdKCEFlt1L8fn0fDaKLYmNgyoUX/hjlc/m/0MOfO3elV/vTPD5/k4L2lviGRXI6LA6fZdOT8+zdtZtVk32o7+SFhYEpp3zWsH/bLmYunv/F9HDujJnc2HuAetlyIYRg45gJnKlYjv7DMhYz+b9Dph6+CylluBDCWAihTHII9DUYjc5j7Z9JfbqctGv50Ugp9wJ7P0Vd/2Vmz55NeHhk0tXrE9CXk8WXKBBCcPXqVby8vNi5cydLliwhISGB1q1bc+zYMYYPH8rGjRuIiorGzs6agIBApFTwarKpeK3el2m647kvnRpFRSYwfdpeXLJWYekfx9BoJPPmHmLxH53w9s7D0j/+pF79Ivz4UwnatV3IjetP6Nq9CgP7r+H3KS3YvOkc0dHxGBrqYWZmRLfuVenWXReiasjgDSQkJHy+h5nJO3mnDej3+sUWBnbMW76L27dvc+TIEYoVK4aFhQWenp4siwlKlT9aT/LrzOk8Cwzk6bMgWnp3omjRosmrzRqNhvPnzxMdHZ1c19fG19eXhw8fkjNnznSPD5iZmbF041qOHT2K79XrFPSszIDq1ZMdVSQkJPyPvfOOr/H64/j73JW9EyNLBBGNPaJFbUqLUmpUjVqt1UlpVdWuVaM2pTatXWrvvXdtYiay903uOr8/7s0iCEK0v7xfr/vKfc59znnO8yT3kzO+gxmjp9LLs316bjZvB09WX9rE7l27qVuv7mPbzi1CQ0PZt2ozA0q9j0IoiElJpLy9L+tWbqRl+zb55hEPkZ94/YmMBg5aggQVA77PrYb/rXqosHNjxtK12erhXN2jc+ZkKyNDRo3jwf1Qoh5E0Kl2+/+UHs5Z+ge7du7k6oVz+NcN4NOH9HDO+Mn8WKYR1iqzHvq5FGD+xf3s3rWLuvXqvfR7CQ0N5dD6DYyoVg+FUBCdnESwe2GWr1nPB+3a5uvhQ+TrYVaEELWyKR6eh5NPAINlIpy5LN94+jVi586dpKamWo4y70Y+vMAjsLW15datW0ybNo3Fi5eQlGSOaH3gwEF++GEgFSt5s2vvANzc7Plt7h6GDlmNTmfMJo1L2rECjUaJ0WhErVZRpUoxChV25acfVzFmTFUUSiVly3pz/vxdnJ1tOXUqhKtXwti46VuUSgX7Dg5hxrRtrF59DG8fP+bOPgHA+bN32Lz5LIOHfICnp3nzKSoqgaWLD7Fx4ysw68vnseRZXqiXja2dHZ+1+4hS1rbYK1XMjB5B2149adm6Nf5Vy7Hs1AGa+lVErVCx4/Y5EgvaUKdOnWxXt69fv853vXpTytYGB5WKWZGRtP70U1q1bZsHd2YOGvJtnz4QGkpxB3tWxcXhVuoNho0bh0qV/a9UrVbT8J13aPjOO498duPGDTwVBdInn2mUsS3J4V0HX8kE9NSpU5S19UYhFKy5tI9Td/+hgosXBZONdGvdnkXrVj53hMT/HJa0A/lkj5RyvRBiL1AcuCWlfDSM8/8ZtnZ29GjTnhJKJ2wVKqbFj6Z9309p2eZDir9VhlVHd9PIsyoqpZK990+T4ql+oh4O+LQvxRRO2Cs0TE/8mXa9u9GqXZs8uDOzHg7o2xvx4B4lnO1YE52Aa8nSDB37/HpYxNo5ffKZRlW3Ihzde+CVTEBPnTpFZdcCKISCFaeOcOTmFYILFsTLoKNH23YsWLM6Xw/TyNfD7Pgm03sroApwFqidJ70xc14I8RGgFEKUAD4HDuZhf/J5CH9/f0BxBakAACAASURBVA4dOmI5StuVzOzLmYZEr9dhY2PDwoUL0Wp1pO2QJiUlo9FIliwbiUZj1t9vBzTh4P6rbNlyFr3emKl9gUIBKpUaV1db+nzekCmTtjB95ic0b1GFdWuPs2vXOby8XDAZJXPndye40mBmz9xJcNVilC7jjVJpvm6JEoX4ZVIHyi/Yy87tcSxatJzIyEhatWoOKChf5ju6dK2FWqNi8cJDdOrclfLly5NP3vGfnYDeu32HufUa4eXkDEBLo4EfZ8yiYpUqDBkzgj+WLmfqilUYDEbqNn2HX7t+ku1gS0rJ4C+/pF9QEN7O5rZaGQwMnj2boHLlKFmy5CP1pJRmX0uN5qWE3585eTLlU1N4r1pGIt1Fp8+wbNEiPu7cOd2vKaemYi4uLsQZ4x8pj9HH4V7YL7e6/dQ+xJi0XIy8zfUH1xlSrjEKIYhOTSTC2sCQbwYwZ1meurBgMJgTpz9uUPuqkOT7PD2MxRx2CFDTUrQfGCKljMm7Xr0+3Lt9h8lVWuLpaF4Bft9oYPTUOVQMrsxPY4fzx9LlzF22BqPRQL1W79Cva+fH6uGgvl/zmedbeDqaTXebGg2MmTaPoPJl80gPJ1HRlEjT2pXTyxYeP8+yxYv4uNPz6WG0LvmR8oiUBNwK+mdTI/dxcXEhSpfKudB7XL4Xwti3a6IQgsjkJEIVgqH9+zNryZJX0pfHka+Hry8P+1UKIQoAv+ZRd9LoCwzCnLpqKWafzRF52qP/KFeuXGHz5s3Y29vTokULXFxcnl4J+Prrr1m1apXlSEmGSSxkNo8FUCptWLx48UO+omYqVS6aPvlMo+WHlUlIsOHIkePp/qP29moMBiMaKxXn/hnL5cuhLF96iOYtqrB//2V695yPtbUaaxsNJQMLM3/eXkCwfft5/vzzMAJBaqoeK6uMyLV7dl+ldBnzwqK7uzu7d+/n7Nmz7N27l2vXrqJUWrNq1SCqVKmS4+eZz8vhaVFw/wZ6SSlDXk13co9UbTI7Ll+ifeVglAoFGqWK97yLsOWvDXz2eV/afvwRbT9+eqC0a9euUUDK9MmnuW0tlZQqun/QkgKFC9OsXVs6deuGEIK///qL36dORWMwkKpQ0KZbN1q1yd2dgYPbtzOpZta4Ki3fKMWX8+ezaeUqREoKWilp0qY1nbt3f+rAq2DBgngEFub0zQuUdw0CIC41noO608xt9WWu9v1xBAcHM1H1M2FXDvKeVxAKIUg16knBQDmfYmw8s4WoqCjc3NxeSX8yExMTw+ABwzl99BIgKFWuKCPGDn6mKJ65S77PUzbMB44DH1iOOwDzgBa5dYF/tR4ma9lz8zyty1RP18MGHsXZ/NdGen7e55n00E2nSp98prVdOsWeLs1aU9CzEO+3b0Pn7l0RQrBx/V/M/3U6Kp0JvRLa9ehKq7atc/XeDu3cztQGwVnKWpYJoM/v89i8eiXK1GSSTfBuqzZ07t4jR3roXMybI/evUtWzBAAx2kS2Rl1lRsufcrXvjyM4OJgpxlTCzh6nRfHiZj00GNBKExWLFmf1/n15qodDB47gn6MXESgoVtaPIfl6+LoTC5TLq4tbfPGLAOOklIPyqh//D3z77bf8+utUQKBUKujbty+rV6/mHYu1R0pKCnq9HgeHR3Moly9fnqVLlxISEoKNjYaUlBQUCiVCKDAY9JazlIB5p3Pp0qUoFMqHWhGcOhmCwWBEpcr47PChG7xVrTqpqXoOHz4KKNFqdWg0Ktq2fQsXFzv0egPW1ubJ5JRJm+ndtyE/j1pPZGQCV6+EEheXjKOTDQ4O1nh7u+Lubk/b1r8yfOSHuLnZM2f2LrZuucCE8X9m6VHZsmUpW/a1TdP9f8vTli5/B7YKIRYAY6WU+qec/9rg6+CAKTmeRccO07lqNQBUCoFB/2y3YDQaUYqMAYtWqyUyNJSC1jY08vXno8rBzF23kcVKFUWK+bN28mRGvfkmdlZWaPV6Js+fj62tLe/mYqQtKU2PWOTrUnXcuXGDhe0642Jji85oYN5fm1ioUNC5e/entjl6yhiG9B/M/lOLsFVao7XRMWTq8Fc2qFAqlUycN53WDZsQq0siLCUWoVLi4+eLEAKlUGA0vnr3FSkl3Tr0QXmjLJXt6iKEIPzENbp81Jt1W5bn2er/qxpwCSFcgRWAHxACtH54V1EIYQ3sxWzqpQJWSimHWD4bDryPefk0HOgspbwvhAjGkpMN8/LpT1LKNS/Q1aJSyuaZjocLIc68QHvZ8Tv/Uj30tnPElBjP8jP7aF/B7B6mUihIei49zPjb02qTibgXirvajroFAmkVWIMly7eySKXEz9+fVeOn832ZOthprEgx6Jg1axE2dra817RJrt2bNJkQDymiXqfj3s3r/PlpG1zsbNAZjMzavt6ihz2e2ubIieMZNnAQm09vwE5lRYLaxPe/jH6lejh+zizaNn6XmBQt9xMTQKXEp0gRsx4q8k4Pe3Xog/+tinzk0AAhBCGnr/JZ+978uTlfDy3n5LkeCiF2krEtpcDsC58nJkRCiG7AKOA65ui3PaSU6/OiL/91du/ezfTp00lJ0ZHx65e0atWKf/75h88//5yNGzcipaR06dLMmzePChUysiGkWTU4ODhY3ouHdEZJZr9QnU6PEAbLccY4Wacz0qXzXH4e2xqD3siUyVtY+edxfHxCKVhIiY2NGq3WiEajxsHRmpiYJACqVi3O3bvRHDx4hZs3wnF2tkWnMxIZkUCFikVp3eZNBvRfijZZx7ARrejStTajRq6j2XvjSUhIwcPDiW+++TZPFubyeXaeuBQspfwDqAA4AseFEP2EEF+nvV5JD58TCbQpGcjRkBvojAZM0sTmu3eo/967z9ROQEAAd3SpRCaaU/tFRUTgamXNllu3qOZfHI1SRdeyFVm3dBlLZs6kZ4UK2FmCWdio1XxWsSLLf/stV++tYvXq7L0ZkqXsj+PHqesfgIuNOfWBRqmiS7mK/LVsRTZO34/i4ODALzMnsWDbEias/pVV29dSuUrlp9bLTby8vBgz61fOqxLw9i+CX7GiaDQa7sZFYXSyzZPommfOnCHhthVe9qXTzQcL2BbHGFaAgwfzzn1FSpHj1wsyENghpSwB7LAcP0wqUFdKWQ4oDzQSQqTZh4+TUpaVUpYHNgA/WsrPA5Ut5Y2AWUKIFxm9JgkhaqcdCCHqAEkv0N4j/Nv1sGXRMpy4czVdD3dGXKfBe42fqZ2AgADuy2Siks2REqMiInFW27Ir4grBPiVRK1V8VKIaaxetYOGM2XQu+RZ2GrMeWqs0dA6sxrLZ83L13ipUq8Ge6yFZylYcOUmDUv642JnznWpUSj59swwb/1ieYz0cN20Kszf8ycils1mx+S8qv2KTLS8vL0ZPn8ZxvQ6von74+fuj0Wi4HROD3t4+z/RQ3LUhwDFDD/3sS+AUXihfDzN4HfSwH2Y/0G8sfZwDnHuB9l6EL4EgKeVbQDUgP4zzS2LhwoUkJ6eQ1SRWIISgZs2abNy4Cb3ehMEgOX36LLVq1SIszByELj4+nooVK9K+/cfExMSg15ssWvmkaYIChULg7u6Gg4Mtjo52ODra8dtvC3B0LIV/ka/w9/uSX6dsJTo6FpMpnr82fkOVYH+srMw7oIkJKWzdco5jx66j0aiYPrMLzd4bT6rOwNkzd9L7ERERz7CfVtOiZRXs7Ky4ePE+VlZqhg5rxY1bk9mwqT+Jian8/fd6Zs+Z/UiamHxeP3IicHrMAzkrwIEMz+TXGrVGw4PkZDCZWH/hPCfioqnVqhWBgYHP1I5CoWDQ2LEM/eorari5o42I4GxEJJX8ihHgYR4AWKlUKAx6YqKiKJgpJQGAs40NKYm5m5e+T7/+9P3kE/45cZLidnb8k5DA9tt3mPd+VtM2jVKFSpowGo05Xpl2dHTMkifwVVO9enX21Qnml907qeLoRZRBy+mUSMbNnZ4n/Xnw4AEanStYZy1X61y5f/9+nvSJV5v37n0yAlcswBxCf0CW7pj/S6X9kastL2n5LLNzsV2m8sxOdtbwwtEQuwMLhRDuluNozGa4uc2/Vg8jUhNBmth05TRntOHUadv8ufRw8ITR/NS3H1VsPUl8EM3F2FDK+5agmGthADRKNUJnJDYqigKFs2qJk7XtS9HDz7t05kLUWYo72HAhLoltN0JZ2jHrLqtGpUSN8V+nhweqVWPE/gNU8/AgQqfjWHwcY2bOzJP+PHjwAEf9o7sLjjq3fD1M685roIdSypMPFR0SQhzB7Hv5qtGlBYKzpMayyoM+/F+g1+stk8as3wej0UhYWBh6vYHMO5h6vYHZs2fz448/MmzYMK5cuZopCm7GDurjJ6GSokX9OXr0KIsXL8be3p6PPvoIKysrzp07h0ajwWCQGI1m/9EWLaugUCjYtGUA3/ZbyvRp20hJMWA0mqhXZySeni6E3o9FoRBcuxqOEBKNxnztG9fD6dKtNn8sP8yOXd9Tr84oGr9bnkaNyjJr5g6GDlnNl183xsvLhcWLZrBwwTy2bduFjY1Nbj7ifHKRp/mANgJ+AdYDFR8SydeaZL0eJy9PtE6OFPywBaNr1sTb2/uZ2zGZTCQlJdHk4w4kJiZw6eAhargVoHlQhjtFZFIiNi4uePsV4VxoKGUzRSe8HhlJ4SJFcuWe0nB0dGT+n39y+PBhbt28yYeBgRTcsYOzZy9R2794+nlRSUlYOTllGWzpdDr27NlDZHgkVapWoXjx4tldIs8QQvDdT4O5fv06x44cpZibK32rVuXQoUMcPXKUatWr4evr+8ztmkwmjh07xvWr1wgILJklpcSTCAoKItFqNin6ytxLOI/EhJdDaZJtblKuXPvnucUX5jmCbrgLIY5nOp4tpZz92LOzUlBKGQogpQy1BLN4BCGEEjiBOfLsNCnlkUyfjQQ6AnFAnUzlVTH7aRYBOkgpDTwnlsTjlYQQ9oCQUiY8rc6z8m/WQ61Bh7N3IVLv2ePbqTHtX1APm3X6iITEBC4ePMRbMWV4t3iGtURUcgK2bs74+PvyT8Rdggr4pH92Myacwn65r4fz/liZroctAwMpuHM7Z26cpl5gRrqSyMRk1A7O2ephVEQklYNfTz0cMGSIRQ+PUM7NjR4WPTx29ChvVXsxPbxx7RolSj6bHk5VzyFJX4VL8ecwSROBTmW4b32dcuXyJip8vh5me/3MaVgUQBkeWUZ9ZXgLIaY87lhK+Xke9Ok/Sbt27VizZi1JSVoyTyD1eoMlzVTWP6mUlFT++ecfAJYuXUpqqp6MtQ/50HsFYCQjf6fExsYKhUKBq6s5JoAQgqFDh7J//35+/XUqyclZU7rs33cZACsrNSNHt2H+vD0kJxtITTWiMppQKhScOD0ShUJBnZrD8fF14/y5O+YWJMTFJlO3XhBBpX1YuqIPvT+bT68UHRERCZw6O5oSJQoB0P7j6jR5dyILFi7gs08/y70HnE+u8rRl4EHAh5bB3b8KbUoKHZYsZsCYsbT76OnBNbIjNjaWPp27UyhBja/aiSu6SJQeGnbHR+F84xoVPb25FRPFwqsX+WrMaAoWKkS/Ll1op9NRulAhLoeHs+jaNYa/hNVqhUJBtWrVqFbN7N/q5+dHz3btkVJSycuH2zHRLLz6D5+PHpleJyQkhK8+6UNxfWFchD0bpv5BYJ1y/DByyEuJTvkiFCtWjGLFinHp0iU6NWtLWaU3DsKadb8u4q0W9fm8/1c5bisxMZG+n/TAJdpIUY0Li3XrmFXAmsm/zcTW1vaJdb29vfF5w5lNa0bxBm+jQME2xhNUqyilHtrtfnU8sylZpJTysfbUQojtQKFsPspxsAhLfrnyQghnYI0QorSU8rzls0HAICHEd0AfzNFqsQzKgoQQpYAFQohNUsrnspsRQgx56DitX0Ofp73H8K/Vw+TUFLqunsd3435+IT3s3bEH7rHWeClcuWF8gLKwksP6UJzvXqKMRxHuxEfy5/2T9P9lOAULFeLrTt1prkvlDQ9vrkaF8uf984yaOy2X7y57Pez1cTtMUlLVz5OQqFjmnL5On2E/p9cJCQmhf4/PqGDjiIfGmjHzFuD3VjDfDxv6Wuth1w9aUdXZDWelmsGzf6Pye43o+803T2/EQmJiIl907UaB5BRK2DrwR9IfzHFyYOKc2TnSQ/dSTkxdP5RgTXWEUDDz3mhKVPfP18NM5LUekjUNiwGzv2rz7E996fR/6PhEnvTi/4DGjRvTokVz1qxZQ3JyCmq1CqVSwZAhQxg6dBhZc3uCra1NumY+GpwtcxoWY5ZyIQSurq5ER0dz5coV0nxDpZTcunWbUqVKkZiYaClPu6aCgweuMHniJj7rVR+lUkGNGiXZutVsGa6x0rBgcU+KFy9E82YT6N23Id8OaIpOZ2DjerCzt2LP7osUKGi2SKlbN4gLl8by+/y9zJm9M33yCeb//x07vcXKPzbmT0BfY544AZVSvv2qOpLbONvaMK5JY1Zt2kS79s++UxUVFcX44aOpZfKmaomSgNnuZsONY9g2akQ0kknHjuPp68vQ2TMJCAgAYPKiRSyZN4/N589TNCCA8YMHP3aF2mzXHoFSqSQ1NRUXF5fnNhdwc3Nj5vKlLJ3/O5OOHaOwjw9DZs2gZMmS6ecM+XoQre3q4ONg/qK+LSuxeNff7Nm9h9p1aj/XdV8mUkqGfDmQTws1oICdOYx4bVmeGWs3cqp+7SzO809ixqRfqax1oVZJc4TfOsC2kDP8Nn0Wffs9eSKblJRE6OUwOnr3JyFKi0RS0fVNDoQuy7MolACmXMx7J6Ws/7jPhBAPhBCFLav9hTEHznhSW7FCiN2Y/ZjOP/TxUmAjlgFXpjoXhRBJQGnMkWyfh8w7nnZAM+BhM7QX4l+th9a2jKzfnA2bNtPu4+fTw3HDfuZNrT+Vfc0TjbeBLXcO49y0IikSfjt6As8ivowcOTVdD39d+juLf5vPnnNnKBpQgonj5r0yPZyxZDnLFsxn1LGjFPbxZfDUAVn0cPi3A+lTvAxFXc1W2w3lG0w9dpA9u3dTu06dxzWdZ0gpGdavPwPLVqaQoxMA78hS/Lx5O6fq1s2xHs6cPIUaSisaVDBHhWwIbLzyD/NmzKTPN092ZU5KSiLsWih9Ar4iKSYZKSHYpQrrI1bm62H2beWJHj6chiUvkVIuyOs+/L8ghGDhwoUcPHiQDRs2YG1tTatWrQgKCmLfvn3s3LkLrda8K6lUKnB0dKBz584AdOjQgUmTJpOSorW0ljZxTJtEmtLL/fz8uHfv3kPmvqb0cxLT3Sxklp9arZEfB6/iu4ErAPDyKoxGo8FkMqFN1lG2rC96vYGtW86xZFlvADQaFe7uNhw5NoxK5X8gMTGVCeM38sWXjVCplGhTdISFxiGlzLJwGB4ej5NTRvaKfF4//rN5QNVqNWV8fFi4ay8JCQnZhpzOjlu3bjHkq4EYIhM5e+k8w8p9iF6vR602h4au51OOidt3snzT2mzre3p60v+HH556nUuXLjG8/yAiQu7yICIUH2dXVG7O1Hi3IV8NHIBS+XBo66fj6ur62AFEdHQ02tAEfPyyrhLVdK3AppV/vZYT0Fu3buGgVVGgcEYOK4VQUNsliC3rNuZ4wHV4x15+LPlelrLaPqUZtWXzUyegR44cwSOxGInRWow6IyBIiErE0/QGu3fvoWXLD55Y/6UgzeYor4j1QCfgZ8vPdQ+fYAmxr7cMtmyA+sAYy2clpJRXLac2Ay5ZyosCd6SUBiFEEaAk5lX650JK+ctDfRoD/PW87f3XUGvUlPbyZdmRy8+sh4O//B7dg2TOXT7L9yU+Ru+aoYe1Cldg5vZNrNicfcBOT09Pvh389I2jS5cuMazfICJC7hEWHoa3kxsad0dqvNeQr7/79rn1sPdX2e8MRkdHY4iKoWhAhoYIIWjsV4Kta9e9lhPQW7du4WqU6ZNPMOvhu75F2fbXhhzr4ZHduxlftWaWsobFSjJw27anTkCPHDmCb2pRkuKTMerMg82EmESK25Vkz+49fJCvh6+FHgoh3IDJmNcXJLAd+EJKGfm8bebz70AIQUBAABcuXGDz5s2MGDGCsmXLMnPmTCpVqsScOXPQarW89957jBkzJt3HvUOHDixdupQ7d+5aWlLwaIgDsynuzZshZEwuRabz0uqk6XXWnVNQkJSkTy9v0eJDpkyZilIJnp6ubN50hsbvlkNKiV6fNcq3wWDCysqKn34awc9jRjBm9AacnOwxmRSoVLbMmb2L7j3qIITg9u1IJk7YysKFK17sYebzUslZZu5/MUYpc5yAXKfT8XWXXjRXBvFNQDMKWTvjJK24E3KLtC+bSZqeazCUmcTERL779HOqGT3w0Enm12jNkKC69CkYhO7weWZOmfpC7WeHUqnElE28FKPJiEqtzqZG3qNUKjHKR/tskKZnC/cvzL+3zJikKZv8VdlUFYL79++jTrHDUXjgKNyx0jkSFhbKi8fNeT4kYJIix68X5GeggRDiKtDAcowQwtOSFxOgMLBLCHEWOAZsk1JuSKsvhDhv+awh8IWlvAZwRghxGliDOb9mrg2OpJQ6IMLii5WPBfPffc718MvOfWigrcxnXm0ooHHD3mTD7Zu30//yjdKEUvli65iJiYkM6P4FVVML46oVzKz0Md8Xe4ceThVI3n+RmZNfjh5mpy1Gk+m11kODKRs9NJlQPpMeikfu3SjN/ldPryq4d+8+Njo7XBXuuCrcsTM4EBoWhszXw9dJD6cBpwFv4J7lOG8iV+XzSpFSUqdOHTZv3poe8fbkyTPUr1+f3r17c//+fWJiYli8eDGenp5s376dt99+mzJlynLnzh2kRRsyNhPTdjYlGWlYMn/X0z7LrB+CjN3TzBgtL4GtrR1xcXGoVEpcXe2RUvLZp7/x86j1ODnZMHrkuvSI5RIYNnQ1np6e9OrVi9u37nPs2GnWr9/K9eu32LRpG1Mm7aVC2R9p1HACFcsN5quvB1CrVi3yeX35z+6AApy+dw+Pov7Y2dnl6PwDBw4QgDtFnMy53oIK+nMs+gZBzl4kJSVhZ2fP37dP8l7HR3Pbx8XFMW/mLI7s3oudgz2tOnWg0bvvZutLtGXTZt609ebkvcu0L1YeK8sALl6rRaM3Mmv8LxzYvoPa7zaiQ5dPciWKl5OTE27+BbkafYsSzuYgICZpYmfMcXq2e9hF4/XA29sbnZOSO/EP8HE0/04MJiM7Y84x6IMxOW6nznvvsH37ORoXzdgh2HzrNPXff3pKHrVazW3TJSoLHWrMwfuMwsBN0zlUqlbPeEe5x6uK+iiljALqZVN+H3jX8v4s5vQk2dVv+ZjyReRiXjpLZMVeQE3M/6/2A10tvlj5AGfD7lKgWNFn0sOiukL4uJuj2wa6FedU/FVK2fuSnJSEnZ0d2+4fo0n39x+pGxcXx2/TZ3N41z7sHO358JP2NH6sHm4h2LoIp0Iv08a3coYepmjRGI3MmvALB3Zsp/a7jXNVDx19vLjw4D5BBc1B40zSxNqbl+k4YshTaucN3t7eJNtaExIdhZ+r2dTVYDLy1+0b9Ouf8zgutRs1YtPh47wfGJRe9teVi9TNQW5WtVrNNd0V9FZ6NGl6KI1c1J1HpWrzjHeUe+Tr4SOUklK2BRBCCCnlQSHExFxsP5/XlH379nHr1u2HIt6CTmdg3rx5DBxozhxkMplo3bo1GzZseCjyrXnSKKUJLy9vhIB79+5n+o4ZeXQSaq5rZ6fCYDCRmmoiYxKaVk+ZpY7JZMLLywshIFmbyi8TO+BX1IOvv1hEocIubNt6nr17fyI42J83SjXl7JnbFPHN8DP39/dPf1+yZEkuXLjMoUOHiImJoVq1aumBkfJ5ffnPTkDDk5JZFhbJ+FmzclwnOjoaF5ExuPkwqA5TDq/kcNQ1AlOLcl+VTOEKAbRp3y5LPa1WS8/2HalnW4Bhpd8mLkXLkokzuXvrNt179XzkOlERkbip7YhPTcbD2j69/M9rp3GysWJ4hdoElAxg+47DfH7gILMWL8zxrsWTGPbLKD7v3Ivjdy/hjB2X9Lep07oRVatWfeG2XwZCCEZPm8BXXXtTJNoZB2HNuZQ7tOjejjfeeCPH7XTv05P+F79g8sWt+Fu5ci0lCqdAX/p36/LUuklJSZTyqMjWyHl4GQNQoOCu4hIBruXyNM/UK0w78G9hJuZgG5Mtxx2AGcDTf8n/B0QkJ7Eq7i4TZud8EyQ6OhonmaFP75d4h9lnlnD83iUC8SfMKhHvysWz1cNP23WiBr5869uI+NRkVv48j3s3b9O9TzZ6GB6Bm8qOE6lJuFtlmAavuX0cV3sVwyvUomRgSbblsh4OGTuGr7t/ik/oHTw0VpyIiaDG+01faz0cMXkS/Xt8RgmNNc4qNceiI2jaueMz6WG33r0YcLEvo48eIMDBkYvxcdgXK8qorl2fWjcpKYmyBSuwPHIB/ooSKISCa8bLlHbP18PXjCxbT0IIn8ed+KoQQgRg1uSCUsrSQoiyQDMp5Yg87tp/ihs3blhM0rN+J1JSUrh48WL68caNG/n7703ZTj7T3j94EMbNmzfp06cP69dvyJRDWQkYsLFRU7NWKbZsPguYSE42MHJ0G0YMX0tykt5yblY/UFtbFXZ2VlSu4s+8+TMpWzaIK1cu4uxsi6urPSEhkaTq9Jy7MIaLF+9z7WoYPj6ulCjhRc2a5rzVWq2W2XNms+nv9dja2tKhQxeaN29O9erVc/FJ5vOyybMJqMU07jhwT0rZRAjhCqwA/DD7PrSWUsZYzv0O6Ip56eVzKeWWp7Xv4unJ76tWPVM0wxIlSjAvdCJlrb2xd7DHzs6O/tXb8cPJxZTp8z7d33yTokWLPlJv04aNlFfaU6eoOcCFm609XQIr8em4SezbupPq9evQruPH6bb2b9aoxow/txBUsCiHI25R17MED7TxPNDG07Nsv49H7AAAIABJREFUfaKNOqzVGioX8mX1lpVUDypDYNkyDBj60zPn7dNqtaxYspQD23bi4OhAn++/xM7OjsjISL4oX/6VJjO/fPkyC2fO587N25SvWoEO3Trj4eHxxDq+vr78sXktJ06cIC4ujo7+/vy9dh1dP2xDQS9P2nfvRlBQ0BPbsLKyYsrcmVy5coWQkBA+KFKEs6fP0PPjHmjUat5v/wHvNG6U7d9KhQoVMDjP4GO3/txJvopJGqlp14Q9hiVUecWJ6TPIlYTq/zUqSSnLZjrebTFz+1fwsvXQ1aswC1avfGY9nB31K6WUftg72GNrZ0efip0ZfWU2lb55lzcfo4d/b9hIkN6VGkXN30tXGwc+8qnGV2N/Zc/W3bzdsDbtOrbP0MO3qzF1xTbe8CjKsaib1CpYkvCUBCJTY+hToR4xMhVrtZoqhX1YvXkV1UuXIbDMC+jh0qUc2rENe0dHPuv/TboedsgDPVw06zfuhdymbHAlPu6aMz1cuvGvdD1s4e/P5vVr6d72Qwp6etGua/cc6eGkObPT9bCRRQ97d+iGRqOhabuWT9TDZMfp9C7cjxsJZj1s6PAea5OW5+vh68VeIUQ5KeUZwA3Yglkz8pI5mCPizgLzTrEQYimQPwHNRcqXL28xo80a8dbOzpbg4GB+//13pk6dypUrV9Bqk8nYzXz4OyRQKlV8/vnnbN68GbMxUeYdTQWFCruw4e/+tGg+ke1bzyMENG1WkaJFPej39RLu3YtBoVBgMknAiI2NhvealGfx0t4olQr0egMtW0yhmCmQX6dsJTVFj0IhaNv2Ld6p/zMDvmtKseIFCQ+L5+aNRBYu6IZOp+Odd+ph76Dj0541iYtLZvDgrzl85CBjfh730p9vPrlHXvqAfgFczHQ8ENghpSwB7LAcI4R4A2gLBGGOJDc9J35dtra2zzTYOnv2LIN79kelE/x+fgsnL59n34XjzLi8iXc/bkm7du2yHWwBXDp9htKuBdOP42JjibgTShk7d5pp/EndfIZPP+pEcrI5bWCZMmVwKV+cCJnC+juXWX3zLDvvXaWYsxsRqckU9CzM7ehIftq4gna+xRgWWIGaMSl0bNiIAwcO5Pie9Ho9vTt1IWbDHnoWDKSpcGXxkNH8c+YsDRs2fKWDrSOHj/Bdxy8pccmeDuraiO2RdP+wM+HhTwwiCJh9n4KDg6lUqRLf9eqDw9HzfOn7Bm8nSkb1+oJ9e/bkqA8BAQE0aNCAGROmcWTydpqmvEm9+PKsG7aUscN/zrZOgQIFaNKpAftS/8BGaYeD2pnDqWup/n75LCYgrxJpSbye09f/CXrLCjsAQoiSgP4J579uvHZ6OOjTASh1gmVX/+L01bMcvHSUBbfX0KxDiyfq4cVT5yjp6JV+HBcTR8SdMEqpCtHA8Aaxqy/SvW3nLHroVsmfSJHC32H/sO7OKfaE/UNxFzcidUkUsujhkA0r+KhIUYYHlqN2rPa59LDPJ51J2b2Z/gE+tHOyYsWoofxz9kwe6OFhBnfpS4V7Snq6VcH+0G0+a9PxmfVwUN9euF88xqCyvryjSGTsl72fWQ9n/vIrp2dtpK2qHM0MAfw95nfGjXi8Hjb6uCEb4ldiq7LDUePMloT1VG5aIV8PXyOklL0sk08wm+O+IaU8lKedAlsp5dGHyp4712k+2VO+fHmqV6+GtbUVaf6bSiU4OTly9OhR+vTpw4kTp0hIeDhV9sMmtRKj0cCmTZtJTTVgni7ITC/w9HRGCMGy5X3o1PltpJR06zKHov4FiI5OApSYTArSdlaNRiMTJn6MUqng3Lk7VH/zJ06euM61qxc5d/YuZ87c4tsBTZg7rwdTZ3zC9m3nmTjhb+LitPz55xocHBxYuXIliATW/fUFzd6vRIeOb7Nr70DmzpnDrVu3Xt6DzSfXyZMJqBDCG3gPmJup+H0gLVz3AjJyVr0PLJdSpkopbwLXgODc7I+UkpHf/khP70b8UL0z9d+oznlDOKvuH6V8m/r0/ebLJ9b3LVGcG/HR5raQRD4Ip4C1A2HaRDys7KlXpDQVTC6sW2WOFCmEYMSEMXzwYx/KNa7LuYIarnnacUOm4OtfFBsbGxYf2UOPN8pR1q0AHrb2vO3lx48VqjGoT98c39eObdvxS5Y0CyiHk7Utvs5ufF2xNmt+X5Q++HtVTBkxgS5eTSjhUgRbtQ3BBUtTR1mG8SPHcvv27Ry1sXzhIho7e1K7WEkcrKwpVaAw31aszvQx49NNQxISErhw4QLR0dHZtnHu3DkSzkfQ1LsOLtZOeNi40sanMYfX72PPnj0kJSU9Uufzr3vz4/zPUTW4h6wdwjczu9D7656cP3+euLi4538oL4A0iRy//k/4CtgihNgrhNgDbCVrLrzXltdRD4f3G8In7u/Tv1IP6gbW4IriLn/H7KNKh1r07f/FE+v7BRTjdqIlfoqEyAfhuGmcCNfF46Z2pKZnBYK0hbLo4cgJY/hwaC/KN6nNJR/BLT8rbqBN18NFR/bwWVA5yrkXoICdPW/7FOGnSm89mx5u306AKZVWZUrhbGuDn5sLP9R8k/WLFrxyPZw6ajx9AuoS6O6Nncaaat6BvOtQgl9Gjcm5Hi5aSHMvFxoE+uNobUWQZwGG1KrAzPFjnkkPtZfCaOVfA1cbRwrYudC5eD2Obdz9WD3s83Vv+s35gri3w4h68w49p3aj11f5evg6IYR4UwixSggxH3ARQtgLIfJqizqNSCFEMSyzFyFEKyA0b7v032T9+vXUrFmDtAmo0WggIiKCJUuWkpSUnal82i5o2s6p+WUymUhJ0ZGWxzPDRNd83onjN7l/PwYbGw3TZnxCVOwswsMTeLvaMLTazD6oEisrNSaTxNnZlqSkFJo0HkuvPg24fe9XwqNmMm5CWzQaFZWDiwHQsGEZlq3oy/adg7CyUhEVFQXArt3bad2mUhY3DFdXe+rVL83+/ftz90Hm81LJKxPcScC3QOZcAAWllKEAlhxbacvRXsDhTOfdtZTlGmFhYdgkC9wLm8Pbly8YQPmCAdyIvc/pa09fUWn2QQu6LF5K0XBXirt4YDJJll45iYfSgZQHsVwPjaSonQvHDx2lXQdzDj6FQkG9+vWpVz8j5dhXn/Zka8hlGhV7g9sxkfgHliZWn4qbxmyqVtK1AHHHc/4FO3P0GOXcCmcpUymU+Nu7EBIS8kx+Qy+CyWQiMTIeF3/H9LLEhARsowVbl/7NvSN3UHlZMW7G+CfuQpw5cpSensWzlDnZ2KLQpqLValkwZy47Vq3H396VO0mxlKpWhe+HD80SMffMydMEKDPcYaRJcv/ufexCVYzuMRZhL/mgSwu69+6e5TrBwcEEBwdjMpkYM3Qso78Yh4coTLi8T50WNek36Jtc8UvLKf8vK/k5RUq517IDmpbo8YolEu6/gddOD62SlLhacqiV8QikjEcgIfF3uXzl6ZOjZh80p/OCZfhEuePvUBCTSbL69mHchBPa0Diu34/E18GNkweOP6SH9ahXPyO+y1ef9mTbrSs0KlaK2zFR+AcGZdHDQFcP4o/mXA/PHTtK5cJZTVxVSgUlnB1fuR4mR8fh5pPx605MSMAx3sSOP/8i8sx1pIcdo6dOfKIenjt2hH6BWfXd2dYGpS4FrVbLwrlz2blmDSWcnAlJiKdk1ap8P2zYI3oYZO2ZfiylWQ+dI2FCn9FIW3i/U0u69Xq8Ho4dNpaxX4+lkLIgYcYwajaryTeD+uXrYd6yALPVhBcwBWiFWWfy0kmuNzAbCBRC3ANuAs+elDifp7J371727UvTRvPk0RyUKO04cxTszBFsM8pVKgUGg4Gs7sTmttRqJUFBQYSF3eet4J8Y+F0TPAo4Mu+3/XgW9qN+vWbMmfNblmsU9fegWLGCzJ2zC1dXe8pX8KPzJxlRajt2qsl3A1ewfes5atYM5MSJmygUAh8fV3Q6Q3puaXf3Aty+ndlYyMytW1G4u7s/x9PKJ6945TugQogmQLiU8kROq2RTlm28dyFEDyHEcSHE8YiIiBz3ydraGq3pUWs9rT4FOwf7bGpkxdHRkYnz57LLOpWBx7by+f71mPRWfBpQB3crezxtnLh85yZq+ydHbxw9eSL64CC+PbaNm4lxhCTFU9jRBWF5BHGpKZiewYzOs4gv9xIeXZEO0ya+UnMzhUKB0CjQGc3PWK/XE3Y3DL3eRCnHknQs0JqqUeXo99mTN6w8fX24GxeTpUxvNKKVRnZu386Vv3YwqnIjepR6k+GVG6E5d4s506ZnbcPHi3BTbPpx+INwTImSFKOJd5yb0NmxJztn72X7tu3Z9mHx/MVcWX2Hj2x70tC2Be1te/HPHzdYvuQV5puSApNJkePX/wNCiIGAt5TyPFAGGG8xV32teV31MMX46Nxda0jBzjFnejh54WyOuscy/Mpavj+7DJNBTSefxriqHSmoceXy3etYOVg/sZ3RkyeiqxJE/2PbLXqYQGEnl3RT4tjUFEyKnOthIV9fbsc9bHYG9xKTX7keSrUyix4+uBeG0WSggocfX5ZqSGOKMLD3ky1vCvv4cjs6q77rjUaSjSZ2bt/Ojc1b+KVWbXpXrMi4WrWxv3qNudMf1cNQQ0Yb4WHhkGQkxWiiReEGfOndiQO/72DH4/Tw98Xc+usmPdy68r5LU3q4dePa2musWJqvh3lMuJRyjZRyKlBCmp0Cn/yFe4kIIRRAZSllfcADCJRS1pBS5ttMvgTGjx+PVqvl0VQoabucaSjJyN1p/jeiUqlQKBQYDKZMdbISHBzMiRMnqFatBlFRSQz4djk9uv/Grp3nadHiQ9auXZulzTR+HtuWcWM2MHXKFoqXKPhIu83er8jkSZtxdepOw3qjqVtrJEW8v8DZ2Tk9qu0nnbsw/7e9HDlyzdw7KZk7ZxeREVrq1XskQHU+rzF5ocbVgWZCiBBgOVBXCLEYeCCEKAxg+ZnmDHMXyBzBzRu4n13DUsrZUsrKUsrKacEcoqOjGfb9jzSv1YiWdZswZ9pMy6pOBi4uLrgX9+RcxI30Mr3RwJbo0zRvl7NUGz4+PoyfMY0FG9fh7OyOWpWRSy5cm8COB5cw6J7skmZtbc1XA75l9a7tfNy3N/MunUNr6avWoOeXU4eo+Z45Ctj+vfvo/MFHNK/ZmB5tP+HUyVNcvnyZL7t1oXXDunRu2RxbBzt2xNzhVozZHM4kTWy6dh6fckGvfKXog85tWHlnB3qjgbjYWGJTk5h+exW34kMYd2wCN2Nvor+X+kQb/nZdu7Dk+gWikhMB0BkNLDx/nHdbt2LNomW0LVEJhcj4k25WvCzb123M0kbNmjUJsQnnSsxNwOyfdkN7j3ihw8vWB4VQUNu+PsvmLM+2D6sXr+Nthwbpg2AhBDXsG7BqwZoXej7PgiTf5ykbPpZS3rQkdB8EHALm53GfcsIr18Oh3w3h/bffpUWdpsyeOitbPSwQUJh/Yq6ll+lNBnYlHKPFR9lmkXgEHx8fJsz6lcWbV+Pk4oYqkx5GpcaxL/YC+pzq4c7tfNynN79dOkdKJj2ccPIwNd81p1Lav3cfXVq1pWWdhnzWvhOnTp4062H3LrR5py6ftGyOrb09f98N52aU2RzVZJKsu3AZ79JlXr0edmzLoisHLHoYR2xqImMvbOJq1F2+2zKXy5F3IDzxiXrY9pOu/HbuBpGJZvNhncHInKPnadSyNWuXLKFDmTJZdiE/KFWKHevXZ2mjZs2aXFXGcDHSfJ242DiuJj4gThop4uCFQiho7FGLFb8ty7YP65aspZ5bvSx6WM+1LmsWrn6h5/Ms5OthtuwUQvwkhCgCSCFEPUCbV52xTID7WN4nSSkfXQnK57lJTk7m5MmT3Lt3D4D79++TEVjIHADo8XnLFZiNIc05Pp2cnCwm/AoyJqdpk0kTtrbWjBs3jo0bN7Jly1ZSUvQkJxuJj9Oh15sYOHAgSUlpwY2MmN18TYTcjCA8PJ6TZ0ZRoVJR1q4+nmlXFoxGEwf330RKBUlJehITDSQnGzEYJLGxccTHxwNm3/VZs3/jg/enUqXiT5QsPoBpvx5kw4ZNz5YfPp8855X/tqSU3wHfAQghagP9pJQfCyHGAZ0wJ3buBKyzVFkPLBVC/AJ4AiWAhx3Zs0Wn09GzfVfqUJzv/VtiMBnZsuo4P90IYcSErEEWhv/yM9/2/JL9Ny7hqrTjSmoYHfp2fWpEwYfRarWU8imGNFoz+Nx61EKJlcaKD8rU5J4x54m6fxjyI5/euEHnHX/horEiVpdKYJXKTJw2lT279zBz4Dg6FamPm78TYYlRDOzyJdZ2KXz/dgWKl3qLmGQtU+bNouGHLVi+/zAxl4+hl0beql+XIQO/faZ7yg06fNIRaZL8smAFEXceEB7xgO6+TajoHIjOpGdLxDHuGG6RmJj42DYCAgL4csxIJo0egy4uAb2QNGnTms49urPtr404eGVd4NUoVZgeGlyr1WqmLZ7JqO+Hs/78Hq7dvU4Zp0q0L9E9fRBlp7InIT77/4+6FD1qpSZLmZXSmpTkV5uC4P9oIJVT0mYzTYAFUsplQojXM8FtJl61Hn72UTeq6UrxdeF2GExGdqw4wpAbgxn5y+gs546YNJp+n33NkbvncFbac0N/j45fdXkuPSzpUxyRbM3om8vT9bBpybpEG55VD6/TccdGXNQaYvU6AitX4pdpU9m7ezdzfxzNZ6Vq4O7rwP34GH7o0QeVtYHBdcpRvGwVYpK0TFowg/otW/LbgYPEHj+PzmTizTr1GTxgwDPdU27QvnMnTFIydOEyIu+GER75gK/L1OatgkVJNRlYG3KOkMQHT9XDvsN/ZuTY0RgS4kiVgnc/bEPn7j3YsWEjDlZWWc7XqFSYjFnT4qrVan5dOJvRPwxjxYUjXL96jUru5elRrkO6Hjqo7Z6ohxoHdZYya6UVKYn5epjHdLD87AikAD2BT/KuOwBsE0L0wxzdO925WEqZvYNyPjli0qRJDBo0CJVKhU6no2bNmtSpU4eLFy9hMmX+vit5dFfSSNrE04zE29s7U7oVRXo5SEqVKsU333zN1q1b2bBhg2WimXkfy9xOcrLW0m7axFeQkqLnvcZjadq0Mt4+LsTHp9Cg3mi+/6E5SqVgwrjN6A1qFIq0emmY21+1ahWffGL+E/6gxQc0ea8JJ06cwM7OjjJlyjxTkL18Xg9ep+WCn4E/hBBdgdvAhwBSygtCiD+AfzAvpfTOaXL5XTt2UizVhcp+5lD9GqWCpn5v8cuRtYSGhlK4cIb/jKurK3NXLOTGjRvExMRQqlQpbG1tn/kmChQogNZG0KBQZVqXroXRJFErlSy5so/ajR9N2P44FAoFcxYtJCIiguvXr+Pn50ehQoUAmDtxOp2LNMDVxuwLVcjejUJGNfW9bCnuYU5Q7mJrQ/+3K9Jv/TqWb9qC0WhEoVC8Mr+ckJAQVi9bRVxULLUa16F2ndp06taZjl07MWb0GG7MOUgll1KkGHUcjblAvC6BB/H3UavVT2y36ptvUnXdGvR6PSqVKl10atSvx74DF6hXNMPq8kL4XYqXNh/Hx8ezZuUarpy9SqnygYyc/DO2trb06tibwGvlsFVl/K5PxZ6gbqu62V6/YvUKXNl2gUDHMulllxLOEfzuq43vkD/geoRQS6L1ZsA7QggVeRvl+0V5KXpYJMmdSp7m74RGqaCxZw2mHv4jWz2c98fvuaKHOltJnQJVaV6yHiZpQqVQser2Dhq+1ybH7Zj1cFG2ejhvynR6vlEDN1uzT6WnowueQkXtIvYUL2A223Kxs2FA7fJ8tX4ty//emid6uGb5SuKiY6n5Tl1q16lNxy6f0OGTzoz9+WfCl2+lWiF/tAY9+0KvEZuaxP2oBznTw9XrHtHD6vXqsfvkKRoGpAeG5nxoKMXeyNDDtSvXcPX8ZQLLlWLExDHY2trSp3Mvgh8EYKfO+F0fjThN7XbZ62GFauX5Z99FSrtkLEycj71A5br5epiXSCnzJiTxk0nLydw7U5kEXse+/ivYsGEDgwb9QHJyKqADJLt378VoNOLi4pweuCdjJzTz5DPtO5P130eaNmbsoJrfW1urKVrUjy+++ILk5BQene+Z25dSoFIpLSa8kswT3BStkbVrjtO+fXs0GmusrNT88P0KdDojsTFafHyLc+XydbL+65ZIKXnYjUSj0fDWW2896yPL5zUiTyegUsrdwG7L+yggWwNuKeVIYOSztn/t0hX8rB/Nq+Zr5c6dO3eyDLjSeNFQ8kIIvh0xmBFffkddpwDcrOw5HhuCvqgLDd5p+MzteXh4PJIbLik6HtdijlnL9An42ntnKbPVqFEa9RiNxldqmrBj63Z+/WEiNW0q4612YNX+Rawru5aJMyeZB316EyVcvbmtvc+SO9uo5FiCas4lcbO2o2/7HsxeuYAiRYo88RoPD8w6de9Kzz2dibx8gtLOBbmREM2BpDAm//wboaGh9Gj9KaWSg/Cx8uHM/vP8Mf9P5v45h+9HfUevdn0IjAmigKowIfprxPpGMazLoGyv+8WAPnQ93oPo6EgKK324b7zFPbfrzOs3J9ee31ORIE1PP+3/jI8wr/Z3lVJeE0JYATmzF31NeNl6ePXiVXzVj/rd+KgKvFQ9HDhqMEP7fk8N6yBcNI6cSbwOJe1yTQ8TY2Jx83PIWpaahJ+Dc5YyW40alVH36vVw23amD55AXYfyFNPYse7QfNb/sYZfZkxO18OAAp5cjw9n9j8HqV7Ql7qF/PCwteXLTl2ZsXzxM+thx27d6N2xE+Fnz1LO3Z1rsbHsiY5m4vx5hIaG0qtddypRjJJ2nlw/eYyOC/5g1vLfGDjie/p+3IsKyQF4WhXgijaEMPc4+n0yPNvr9v32c7of705kZCTeam/u6O9w0zGEOd/Mzfb8l0K+Hj6CEKLTkz6XUi540ucvAyll9nmb8nluxo0bZ9ltTJuwCXQ6Pfv3H2DRooX079/fYsafNvlM211Mm5CqyNilVACCS5cuI6XExsYKrdZsyWBnZ8ubb1Zlz569lii6wrJLaiJtcmlrq8LT05mqbxZn2dJDZExgM3ZXAfR6A3/9tZ4PWlZgxqyMTfnw8DhK+PfD1taG5HSLMvMkVkrJtGnTqFatGjVq1Mjtx5hPHvFv3iF4KiVLv8F17YMsZVJKbqaE4+fn99h6Op2OI0eOcPToUfT6nKcSTExM5MCBA6g1GmauWoxts/LcLefI+0N6MXnOdJTKp6bryxGOHi5EJsdmKbNTO3AzPqu5VkJKKia15pUOtgwGA5OG/kL3wq0p7/4GxZyK0NKrEbpz8ezbtw+AkmXfIN7BwAnDdeq7V6BhwYqUcPamimsgH3vUZuLwsc98XUdHR+avXE65Hm24XMKVwq0b8vvalXh5eTFp1CTe1tXiLbdqeNv7UM21OsHJbzJt/HSKFCnCss1LqPhVWYyNkmkyohELVv2OnZ1dttfx8PBg+d9LqPNDMKJJAvUHv8Xyv5fg5ub2Qs/tWcn3ecqKlDJWSjnFMonDkqbkeh5367UisEwpbuqyZj2QUnJLF/aS9VDNnLULcWtTiui3NHw4qhtT5uaiHrq5Ep6YNRiPvcaOG3FZU6skpKRiVFu9cj2cMmwCvf1aULFgIMVdfGhbpD7Gf2LS9TCgTBDR1oID8fdoWqQUTYv+j72zDo/q6OLwe1ezGydCQoIELe6lQHEtVqC4F0qLFAoUivMBpcWhuBT34g4laJFSirsEK5ogCSGyPt8fG1sS4kAp+z7PPsnOzp2Ze3f3t/fMnDmnMB95+VLRN4Buecsw7eekc3Imh4uLCwvX/kahDu0575cNj0YNWbRxA35+fkwfN5XP1GWpmq0suVz9qJ7tY2rKijNn6ixy5szJih2rKfBNGUI/FVQf0ojF65clq4erdqyi/ICKRNfUU/GHSqzcvsquh++ez7BOUlUFqgE/A02A0kCZdzEgSZKUkiT1liRpfczjW0mSkl/it5Ms1r2eiT/TRqORjh07vrJqKEuibqzxKY973WSyIEkSDRrUp1y5slSrVoVFixaiUqkT7O2E+NVRM1qtkhYtynHl+iSWLu/O8b9H4eiUcKtSrLFqNViNRh0NGpWwGYm3tyslSuamSJHCODo6EL8ya/2d+Oefe9SqVYtLly6l8SrZ+bfyb3LBzXSqVqvKounzOPrgAuV8C2EwG9l57wSFK5d+bdTDv47/xZjv/0ceWTYEgjEEM2raT5QsVTLZvtavWceyXxbwkTo7ESKaZ446piyYjp9fpmZIAKBb/15M6Teadv7V8XXy5O6LxwQrjWx8HEHAw2AK+3oT/DKS6X+dp1Ovt5sK8datW/hIHmgUtvsxi2s+4vDvh6hSpQp1PqvLqnlL+SfkBk3ztUdCRqgxAidXZ7J6+rD+SvryZatUKho0akSDRo1syi+dukxnF9vo8wXdCrHkmDVMuIuLC207tEl1P1qtlmYtvoAW6RpmhhFIWD6cG6lUIUlSOPHTunHFQghnSZKOCCE++GnTqtWqsnDafP4MPkdZryIYzEb2PD5Gkaolk9XD0X1HkgvrvqAfFY8YPX1MqvRw6dSF5FPkJJJoQp0jmbrwzejh1/2+Y/rAEXQtUB4/lyzcDg3hoczC+vsR5HoQQpFsXgSHRzLt6CU6fds/0/tPjlu3buErc0ejsN2PWdo5H4f3HIzTwzULFvPwwS3al2+EJEk800Xh6OpCbl8flvy1M119v04Pr5y5SD2/1jZlRb3yM+WYNXKti4sLbdqnPjuGVqvlC7se/ttwAooIIcIAJElyA1YLIXq/wzHNAZRAbCjm9jFlX72zEb3n1KxZkzt3FiWIWAtgwWKB6OjYSOaxP4ux35HYwEKxf8HWMJWwWCwYDAaOH4/P+NWzZ0LP6fi6KpUaISyMGtMsbgtAqVIBHPxjGGVLDSdhXtFYd1yDwcT5c/9Qv37874jBYCLoxiNhmiriAAAgAElEQVQOHjzKihUr+PnnnxHC1hVXp9NRsmRJli9fTsuWqd/CYeffyX/aAFUoFMxduZD502czYd8WlGolDTt9QZsOSf+4RkZGMqbvCLplbYqL2ppuIEwXzoheg1m/fwvqV4I6xHLjxg3WTl3K9wEtUcqtl/TOi4cM6tmf5ZuTjh6YET6pUJ4fZvzIr1Nn8+RuMNlz52TKT3NwcHDg12lTmR14AjdPT9oOHknlKlVSbjATcXV1JdyUOHBGuPElWbyt+eYcHByYu3oRjSrVJSj6EW4qZ9y93HHP4o7ebESuSvljKYRI9aZzlYMKnVmHRhGfBifaFIUmhbQ4/2Y+oJn8VCGEcEnmtQ/e+ASrHs5btYB50+cwbe9aVCoVjb5ukqweju4zki/dWuKssrq4hulfMKznEDYe3JysHq6evJxv/dujlFm/y3fDHzCoxwCWb1mV6ef1SYXyKKb8zMJps3h65wR+AbmYuGS+VQ+nT2XmztO4e3jSZtBIKlepmun9J4erqysvTJGJysMMEWTxtrrVOjg4MHvFEppUq8XVF0/IonHEzTML7u7u6E1GZCnsA4W06aFSrSLapEerjJ8kjDRGo3FM+x7ffwt2PUxEHkCf4LkOyPVuhhJHWSFE8QTP90uSdO6djeY/wNChQ1m3bh3h4RExEWUT77uMXaW0vha70gmJc4HGf4ckSbJx+79w4QKhoaHEu9zGR9c1GMwolXJUr9y3HToYm6szdiUzfgVWpzMxfuw2yn2Sl2rVChEeHs2gH9ZSpkxZ8ubNy4YNa1Eo5BiNAtuVUAmj0ULnzp0pVKgQRYsWxc77y3/aAAXrbG7/YYNg2KAU6/7xxx8UleeOMz4B3Bxc+EjKwbFjx6hWrVqSx21fv4WqLsXijE+AXK7ZkO7+zb1798iePXuSx2WEMmXLUGbVokTlY6fNyPS+0kLWrFlxz+fFhfvXKJqlAAARxiiOGs4wp0X8DJq7uztDJ45i448L+TL3Z8hlMoQQbL9/jPqtm7y2/VMnTzLj5wlEPH2KUCpo0q4NbTt1TPbmq1mnL9g3NZB6ng2QSTIswsLe0EBaDX1/Z9DsN1y2xAQdKgYk3Aw4DmuE2dv2fHNWXFxcGDBsIAOGpRz59Y8//qCgyBtnfAK4qV3JJ3Ilq4fb1m+hkrZUnPEJkNPFD/Hozzeoh2Ups2JJovKxv/wL9DCPN+eCb1DcKx8AEYYoDkVeYFbzvnH13N3dGTz2R3ZMns23eYrE6eG6a2eo3+L1W5lPnTzJzHHjiHj6DKFQ0LhdW9p2TF4Pm3ZswbbZu2mes2acHm57+Act+rV+7TH/dux6mIiVwHFJkjbHPG8KZP5seNowS5KUJ3ZrhCRJuXk1Ao6dNOHv78/58+eZMGECe/fuxd/fnz179iAS+gElMBglScS9ptFomDRpElOnTuXWrdtYLLYZA3r06BH3/6RJkzCbY/eOvvqWWYMOTZ64g3ET4jVk+rQ9xBudCaPvWl1xIyJ0NGk0BblchsUi0ahRA1aunM+2bdtw0FiQy63Gpu15WP/q9UbmzJnD7FfyGtt5v/jPG6BpQa/XoxSJL4kKebJ7n/Q6Pc6yxLPUapkyTXum/iuMnzmRId8N5MjV0zjJtYQqwvlh0hCyZctmU69O3To8/OcB45b+hp86C4/0oVT4rApfft05yXZv3rzJ+H4D6VfkU7zzuqA3GVn621aWGo10+rrra8fTql0rQh6FsHD9fLzk3jwxB1O/Q32aNHu9oftvRggwfzgJ1VPLbqxTpAnzRXwEfA+sAuwGaBp5nR4qRfK6po824JqEHqqkD1MPx86YxNDvBnLgxjmc5VqeySLoP2FYIj2sXbcuj+7dZ9DKNeRwdONB5AvK1a5Ox6+6JNnuzZs3mdh/AD+U+ZisRUugMxpZvGGjVQ+7vl4PW7ZpxZNHIUzeuAoflQePDM+o26IBjb+w6+F/BSHEGEmSdgCfYr1z7yiEOP2OhzUAOCBJ0q2YMeXk3aeGea8xm83cvHmTzz77jJ9++glHR0eqV6/OwYN/JDBCre6vMpkci8WMJEkoFArmz59Pu3bt8Pb2jnFnjTUWrQceO3aMAgWsiwjbtm2LaSvh6mnsqqmc6Ggzs2YGcvx4EJ/VK87+fdd59DB2X378XlHbPacSkZEmwBrwaNq0Wbi6unLi7xM0blKCW8WysWb1nxgM8YGOEp63df+rnfcZuwEKPHnyhJWLV3LmrzMEPbxEBa8SaNVW9yS92cB54y0GfvLJa4+v2bAOswPHUdAjd9zMc6gunKeKCIKCgpg1fiqSJFGv+edUq179vc1XZDAY2LJ5K79vPYirmzPturSgZMnEe8FcXV2ZtWQuT58+JSIighw5ciSZ7kCSJDp/04XWHdrw6NEjvL29cXJySlQvllULFtEmZxG8nazelmqFkk6FyzFk9W90+KrLa1MqyGQy+gzsQ9dvuxIcHIyPj0+6UkpkBkII/vjjD3b8thGzyUTtJg2pVad2mtNB2M5w2gE8hBA2H0ZJkk4LIRq+qwG9r1j1cBVnjp/mevAVPs5SGq3K6q5uMBu4bLnB8GT0sFaj2sz4fSIF3PMk0MMXPFe+JCgoiJljpyLJZNRv8TnVqld77/UwMEYP2ySjhzNTqYedvu5Ky/btUqeHCxfSPn8BsjpbV6gdlEq6lChF/zVr6NAleT3sPaAPXXrY9fC/jBDiDHDmXY8jFiHEPkmS8gEFsFoUV4UQ+hQOs/MaTp8+Tb169YiKikaSJEym+FXB8uXLo9MZ0Ol0yOUSZrOExQKgsE7YmAVLliyhXbt2TJkyJea1+Ei6QggGDhxIxxhvCqv7LcTvHY01VOOPiY42c+TwNY4cvoZWq6JK1QIc/uMaen2sASlDpZJhNgvM5oSBjCSEgLVr19KjRw9y5sjJ/gOHWb7yayRJYumSP7AN7SDQarXUr1//jV1bO2+HD94AvX//Pt1bdKe0sTTVHatjkhsYdHgSjfPVRKFS8LfuKl8P+RYXl9duMaN06dIUqFuaObs2UdIhDy8tOk4ZgwgokocdPy+glm8xhBBsGj2Hk8f+4ofhQ97iGWYOZrOZrh16En7OiRyqT4gwRdD/wFi6DmlGq7ZJR5/w9PTE09MzxbY1Gk2q0j3cu32HBt4f2ZSp5Ao0yNHr9Wg0ye/pdHR0zHBaiYwyfeJkru88QgP/YshlMgInLeLogUOMnjA2Da18UNEcU8uSJMqWve1BvO/cv3+fbi26U0pflsramujlJv53fCwN89RBoVBw2nKRbkN7pKiHheqVYMHOdRRT5COCaM5ZrhFQJA9bRi2hqmdJQLB2+Hz+PvoXA0cMfnsnmEmYzWa+6dAT3Vln8qg+IdocyaADY+k8pBkt35Ie3r9zh6Y5bDNbqBQKtJLMrocfOK8EZYv7+y6DskmS1BNYKYQ4H/PcXZKkLkIIux9lGjEYDNSuXZtnz0JJuCrZtWtX6tWrR7NmzVCr1Tx+/Jht27ZjNptIuIposQgOHTqEwWDg4sWLvLrCCBIvXrzg5cuXuLq6ki9fPm7cCIp5LXmvaQcHJd/1qcvoMc0Z9MNqZs4IRJIk9HpzErlDrej1BsLDwwFo1aoVo0aNoEypYdy+FZJggtLqjqvROBAQkJN27dql8mrZ+bfywfutzJo4iyqWKpTMUhI3tRst8rSiaUAb/lRdJ9dXpZm3bQkNGye/iCJJEgNHDGbE0glkaVuYIj2rMWbOBMKu3uPL/NXJ7uJFDldvuhSoybk9R7l37166xhoeHs6uXbvYvXs3L1++TPmATOTAgYOEXlBR1KkuriovsmoD+MShA3OnLEWn06XcQCZQpEwpzj62vXYv9dGY1AocHBxec5QtBoOBQ4cOsXXrVh49epTyAakgLCyMnTt3smfPHiIjEwccieXx48cc3xZIzyI1yOXuTXZXTzoXqszDExe5du1amvp8W2kHJElqLknSJUmSLJIkvTZ8vyRJdSVJuiZJUpAkSYMSlGeRJClQkqQbMX/dE7w2OKb+NUmS6qRzfO0BhBDTEpRpYsqbpqfND5lZE2dRyVSN4u6lcFO70zx3a5rmbsMp7WXy9yzGgu2LU6eH/xvMqOVj8emUn5LfVeKnueN5fukBbXPVxt/ZG3/nrHQIqMuZ3X++t3oYcUFFaae6uKm88dUEUE3dkflTlr09PSxdmrMPH9iUhet0GJVKux5+oHoYixDCRQjh/OrfmNfeVVC2rrFReWPGEQq83lc8FaT2/XgfEUJw+vRpdu3axdOnT21eCwwMjNnOEHsLb3WzNRgMbN68lUWLlrBw4UKuXbuGwfD6RWZJksiRIye2K4zW9tRqdZwHxpQpU9BqNcS63IIU46Xw6t5OkMtldOhUGUmSGD+xDVeuT2TOvC9Rq5UULJgDtVoBWFCrZbi6qlCrrfMjtWtb80K7ubnx5ZdfcfnSAyIjTQki4VpQq5WMGjWS48ePpzjBZuffzwdvgF48dZG8Lnltyop5FENhVNK2QzuyZk2cuP115M+fny+/6kyzFs24desWRdW+Nu5lkiRRVJONc+fSHvht7569tK7Zgv0jd7LvfztoVbMFB/btT3M76eXPQ3/jLQrYlMllSlzM/ty8+XbSLbb9shM7Q//h6D830JtM3HoewsQzB+nWv1+q3Phu3brFFzW/YF3/NRwZdYhuDb5hzrQ5GRrTzm3b6VSvGWd+Wc+fE1fTpk5j/jx6LMm658+fp6iTb6KxlnTKxplTqd+eIwRY0vDIIBexGnJ/vK6CJElyYBbW3HOFgNaSJBWKeXkQsE8IkQ/YF/OcmNdbAYWBusDsmHbSyhhJkrLFtFlKkqRZwDmgFPBtOtr7oLl46hJ5nPPZlBXNUhy5KZ162DVeDwsq/BLpYSGlf7r0cF/gXtrXbcbxiRv4c8J62tX5ggP7354eHj/0N9kstt4YCpkSd5PfW9PDNp06seXxI47cvoXeZOLm0yeMPX6Mb/p/n2o9bF6rKZsGr+TE2H30/Lwrc6ZlbDFq57YddKrXnNNTNnJs/Bra1LbrIW9XD2PHkDO5R3rbzSAyKcGbHXN+qmTqp4YU34/3kQcPHlC0aFEqVqxIw4aN8PLyIk+evJw4cQKA0NDQGLfZWBKmOZFhsYBOp+fixUskDgJkNRJr1aqFUmk16LRaBxIakpIkUatWrbhVyQYNGrBlyxbKlSuLu7sL5ct/wu+//06rVi1RKCTUagUymfWtlckkHjx4HtdXjhye1G9QErlczp07TyhfIS9arYI+feuwem0v+vb7DK1WhSXBCe3atSvm/KQEDzlyuZzPP/882a0Jdt4fPngXXNcsrrwIf4Gb2i2uLNIYidY5Y3tiPD09OS6iEpU/MUfi5eWVprbCw8OZOmwS33i1wyEmv2Z5fWkmDh5P2f0fv5UvY7bs3lyyJI7jEk1oqtzKMgMPDw9mr1rOsl8X8PPx4/j4+dF/+iRKlCiR4rFCCAb3HEQTWUOyellzHlYSFVmxbA0VqlagePHiKbSQmKdPn7Jg3DSGFvoctcIadKWGvijjB41kTeDWRKsQXl5ePDEl/kwEGyMp6JP6G3vr+bwdlzMhxBUgpRvaj4EgIcStmLprgM+ByzF/q8bUWwocBAbGlK+J2QN0W5KkoJh20poEti/WcP4CcAF6A72FEPboiunANYsL4WEvcFUl0ENTJFrnjM02e3p68lxKvBr2TESkSw+n/28C/fM0RqNUg4CqumJMHfozZQPfjh76ZvfmpuWfROWR0tvVw1krV7Ds1wXsOPEXWf386Dd1Sqr1cMi3A2mpqYOPp1UPq4pPWLRyPRWqVky3Hv469hcGF2icQA+LMW7QSH6z6+Hb0sNYtiXzmgS8i/wVvwNrJUmai9Xa6YY1eFy6SeX78d7RpEkTrly5isVixrpOJOfWrdtUrlyZwMBAqlWrhslkwNbDOj7Nia1BCvEBgCTkcjn+/tlYsGABAF988QXh4eEMGjSI58+fo1DIcXbRYLY8JHfuXMyeM5fWrVpTs2ZNatasaTPOmjVrMmnSJM6cOcOCBQvYsmUrL1/q6dt7OfsPDcPd3RGDwUSf3suoXPlTihYrztIlC/hlegc6d6kKQO3aRcmTNyuDBn3P3r2HAGyM0YRIkhQTjdfOf4EP3gDt0KMDiwYvoplnM5QyJUaLkV2hu2gzrE2G2q1QoQIzFJO4/uw++T38Abj67B8eqKMoW7Zsmto6evQohaV8OCgciIiIIPhhMJIFXF9o6PXNt8xbPB+VKqMTicnTpFljVszviI8hPy4qT4QQ3Io6RZ5yPmlaFckoXl5efD8k7XvG7t+/j/y5jKwe3nFlMklGOXUZdmzYka4brkMHD1LeKSDuZgvARa2lsENWTpw4QeXKlW3qFy9enKeOggvBdyma1ToJHfT8EZctzxn2adq8otJ4w+UpSdLJBM/nCyHmp6nD5PEDEvpR3gfKxfyfVQjxCEAI8UiSJO8Exxx/5Ri/tHYshNgIbJQkqRbWiIrjgTKSJC0SQtxIa3sfOu17dGDRwMU08WgRp4e/h+2g7fCM6+F09WSCQu+R192ahuVa6D88cnyZLj0s6ZATjVJNZEQkwQ8fI1kE3iEKen3Tk3mLf30rerh6fkf8DflxjdHD61EnCfjk/dFDZZgMH19bPfzUsRQ7M6KHjrlt9NBZraWwyteuh29JD2MRQhRL77FvkIHA10B3rJbSHmDB2+pckqSvY/rHy8uLgwcPvq2uUyQiIiJuPAaDgdatW8dEpgVboxLOnTvHuXPnGDNmTBIt2dZN/ByUSiVFixbl+vXrXL9+HYCAgABWrFjBpUuXKFDAB7PZgT7fDeCbrw1cu/aYwMBAlK/JR6zX67l58yZVqlShUqVK1l4liWWLI9Bo9Oj1JsqUbkahQoVQq9Vk9fYhT0AuDh+M/87mDqjDZ58VYPfu3YSFhdGrVy/Cw18ihDWAkb+/H5MmjUelUhEcHExwcHBqL+0bIeH79W/g3zae1PLBG6C169Ym7HkYi2cuRmvWEiVF0fLblnze9PMMtSuXy/ll0RxG/zCMdZdOAgKvPP5MmzUvzRH+AAQCvUHP43uP8FBkQa6Q4SZ35dnJcCaMGs+wn4ZnaLwpkSVLFmYtG8+wfj/y4rEBs6SnbM1ijBo37o32m5m860CJMpmMqQvmMHrgUNad3YpcknD19+GXNE4gCNLsSvZUCJHcfqW9gE8SLw0VQmxJRftJ3f2lNML0HPNahBCBQKAkSW5AG2ClJEmGd7jf6b2kTt3avHgWxpKZC9CYtUTLomjVK3P0cNqS2YwaMJwtt/5EIPDO68eMiXPTp4dCYNAbeHzvId5qV+QyGR5qJx5dfsrEH8cx9McRGRpvSmTJkoXpy8YzvN8YIh4bMEl6Stcqysj3Sg8Tf93EKwnpM4XXNGfXwwwf8/rGJKlKUuVCiEOSJJUWQpxKb9vpRVitibnAXEmSsgD+qfFUyYT3I7b/+cB8gAIFCoiqVaum9tA3zsGDB4kdz5kzZxgx4n9ERLzEuoIpiN1fGU98JFpJIsZQg9j9mbG5NuOfW1EoZHz1VWd69+6daAzTpk3j/IVt9OrTmcMHLVSqGg3A+g3WxfQ+ffrY1L9y5Qq//fYbc+fOJSTkWUwk6th+Y91mQaVS0rp1K/r2teY9btOmJYH7+/PRR/FpqK5efcjIkSMxmwVGoxkhBBaLGaVSicFgZMqUSYwaNYrAwEDKlSvHuybh+/Vv4N82ntTywRugAC3atKBZq2aEh4fj7OyMXJ7urRfcvXuXhTN+5er5q+T5KA/f/29wXL43R0fHdLVZsWJFZopp5AvOhbPkhFySEW3WcVF/jb5Fv2VR4Cqih0W/8U3ZhQoVYuPu1YSHh6NSqVId6OLfgL+/P5YsFh5HBeOjta5QWISF47q/GdYsfcZ71WrVWDFlLlVMRXBQWG+YwvVRXNIHM+I1Iunp6cn0hfOIiorCYrGk010wc6M+CiFqplwrWe4D2RM89wdik3QFS5LkGzPb7wuEpOKYdBMT5GI21j1URTLa3odIi7YtaNY68/RwwYwFXD1/hTwf5aX/yEGZoodz9VMpEpwNV4UWuUxGlEnPqfBbDK/aman7thE95O3o4Ybdq95bPTS5CR5FhuDraF2EswgLRyPPMKjZ/9LVZtVq1Vg+eR6VTUXj9PClPopL+kf8z66Hb1sPv0+iTAIOAe2Bt26ASpJ0EGiE9b7zLPBEkqRDQoh+yR2XCe/He0XhwoXj9lPGG58J82/KsE2ZktDge9Wej3Xhlayutc6ODBmSdBYGnU6Hs7M6UbmTs5ro6GibssmTJzN8+HAMBmNMhN1YQzfWMLZGvPX29qZ379788MMPccd+1fVr+n63it/W9cDFRUt4eBTdui5BpzNgMtlOgAlhoXv3b/D39+fOnTtkyZIlmStn533jgw9CFItMJsPNzS1DN1s3b96kZ4tueJ5wpJO6GTnOe/J92++4detWum+2AFxcXOj30wBmP1zK4Zcn2Bd2hF+frqJhvvpolVpc5E5vNQqki4vLe3WzBVaXkLGzxrFF7GDX0985FHyYX0MWU71TDYoVS5+3koeHB18P6cvPl7ey5eZfbLz5JxNv7GLQuFGo1YmFPCFarTb9e9UEWCxSqh9vgb+BfJIkBUiSpMIaTGNrzGtbgY4x/3cEtiQobyVJklqSpAAgH3AiMwclhLiYme19SGSeHnbH87gTHZUtyH7Wi35t+mSKHn43eiDjrq5j75Oz7Hh8ksm3ttC8aE0cVRrclI52PUwBSZL4eeZ41un2sO3xPvY/PMas+yup3K5mhvTwm6F9GXdtC1tvHWfTrT+ZdHMHg8fb9ZC3rIdCiEavPogJyiaE6JPC4W8KVyFEONagQYuFEKWBD8q4TA0qlYp58+ahUFijxcYbdbGrnIlTpmDjuRAbqTY2Wi3kzOlP586dOHfuHNmzZycp6tevz9rfTvDsWbx2Pnv2knW//U3DhvGRz7ds2cKAAQOIjjZgNie1SG/tPyAggCtXruDh4cGECRP4888/EUIwfNgIcuUqRZ5c/an86Vjy5OqPwahFJrNdrY01tHPlyoWXl5fd+PwP8sGugD558oTA3/dg1BuoVqsGOXLkyHCbsyfOooGmJrldrftZ8rgG8IVCy/SfpvHrmoxtdahRqwa9f+zLrknbKO5WhNpZaqNRaIg0RhGpik428MX169c5fOAwLm4u1PmsTrI5/F7HkSNHWLt4I7poHfW+qE3Dxg0zdHOaXsLDw/lt5WrOHD2OT3Y/WnfuSL58+VI+EMidOzcb9m7g6NGj7N+3D5+gLNy4dImDBw5QpWrVNAcyOHfuHEf3HSBrLl8is3nyadUq/FC9+htP6i4AkfQe/UxHkqQmwAzAC9ghSdJZIUSdmMizC4QQ9YQQJkmSvsUaZEIOLBJCXIppYhzWwBNdgH+A5gBCiEuSJK3FGpjDBPS0Bw56d8TqoUFvoHom6mF9h5oEuOQCrHrYVFE/U/Swes0afDuyPwdmbqCMdwHqZ62KVulAhCGacLk+ZT08eBgX14zp4bolG9BH66nb9N3q4dqVqzhz7C+y+qddD9cFbozTQ68gT25cvMzBAwepUrVKuvTwyN79eOf0JdLPg0pVqzDQrofvRA8lScoHNAScExR3iwkAdFAIcSi9bWcARcyqbwtgaGY0+Lr3IzPafpe0atWKfPny8fHHH8dMmiQMNpTYTV6hUCBJMozG+BVPK3JkMhkNGjRg5syZyfZZpEgROnfpStlSIxkyZDj16vUjOtpA3rx54ibYgoKCaNGiRYy7bcIxJByTwMFBReXKlcmRIwcWi3WfqIPDz9SuXZt169Yxf95CRo0cw40bN8iXLx+rV6/m7JnE88WSJKVri4ad94MP8p3duyeQrxq05e7CE4SsOM/3LbqzbOHSDLd788oNAlxsb9x8HbMSfC9z8qu1btcaRT41L4kkyhTNtbAglj/5jT4j+iX5JRVCMOHHSQxoNpzL0x5xcPQZmlVrzamTafO+mTNtLpO/mUvWk4XJd6UCG0fs47uu/RDi7e6qfPHiBV1btCNq+xlaKgqQ77qeIZ16cvxY6gMFKhQKdm/ajv7oTb6QFaB6uDdr/zeD6ROnpmksWzdtYmrvAVQMg85uufG68ZiNy1bGzFq+ed5W3jshxCYhhL8QQi2EyBr74y6EeCiEqJeg3k4hRH4hRB4hxE8Jyp8JIWoIIfLF/H2e4LWfYuoXEELsytBA7aSbvXsC6Vy/HbfmneTxkkv0bdYjU/Qw6HIQuZxtMz74OvoQ/M/jDLcN0KptG8w5nQg3RxFp1HHpyW1m3dxBr2Hfv1YPJ46ZyJBWQ7kz9x7Hx/9Nyxqt0qWH07rPIce5QhS+WZ5tI/fS5+t3pIct26LbfZK22jwUvBPF0M490qWH0Ydv87mlIJVDfVkzfGaa9XDLxk1M6jmIssEq2joUxP3SUzYsXWXXw3enh+uxRgV/meBhAiIAQwbbTi+jsRrlQUKIvyVJyg1kKFDc696P/wKlS5fm448/xtYNNzb3ZmxUWxNgoXDhwuTPHzvxZPsZN5lMXLhwIVV9/jRmLH5+eYiM1BMdbcYaffcu5cqV48mTJ0yePBmT6dV5EXmCMVlwctJSqFBBtm/fTkREFFFROsxmQWSkjj179rBq1SoAfH19qVy5Mr6+vjRt2hSrZCfUUIFMJvHFF1+k8orZed/44AzQqKgopo+cRJ+AZlT1K8WnfiX4Lncztsxfw4MHD1JuIIbYyF+xeZIAPLN68VT33KZeuOEljq6ZkxZAo9GwcO0iivYoxVGfU4RWiGT8qklUq1Etyfpnz57l+NozNHH6kmLuZfnYrQqN5B35X5/Rrw1z/SqhoaFsXryTek5t8dH4k0XtRRXnBjw5+ZLTp1Ofqy0zWL1sBZ8q/KiRsygeWmeKZs1F7/w1mDZmfKpv/i5fvkzIqes0y1kOHyd3srt68XXBmluC1foAACAASURBVBzbuoeQkJCUGwCMRiNLps1iYOkaFPDyxcvJhSYFSpA7Cn7flfR9g06nS/R5STcCLEJK9cOOndcRFRXFtP9Nppd/Kyr7lqWCbym+zdmKzfPWZlwPfTyT1kO39LvfJkSj0fDr6iUEdPiUnerr3C0iY8ySX6hWvXqS9c+ePcvJDadp49GeUp6l+dSzEq0c2zKq36g06eHWpTv43L0Nvlp/PNSe1MhSn+enw9+JHlZR+1Art1UPi/nkpF/hqkz7aVya9DD47xs09S+Pj1MWsrt481X+2hzdEphGPZzN90XrkN/DDy9HFxrlKUPOcDm77Xr4rjALIUYKIabEPoAnQojJQoj0pnbJEEKIdUKIYkKIHjHPbwkh7NZFMowdOxaNJta9PzbVioz4lVDraue5c+e5dOlSgnrxyGRSjCGbMlevXuXs2TMx+mHdV2qxCF6+jKBChQqsWLEiRitj+4fYvZ9yuYzu3b9h2bKl/PDDD+h0OmxNDInIyGgWLVqUqN9cuXIxefJkHByUODgoUautf8ePH0dAQECqxm7n/eODc8E9deoUHyn9cVDE70mRy2SU1eTj4P6DtG3fNsU2Vi9bwZr5S8muzsJjXRjFKn/M4NEj6NKnK5N7jae1d2OcVU5EGqPYELKDL3/6OtPGr9VqadexHe06tkux7u9bAylEGRtXKmelK85RWQgKCiJ//vwptnHlyhWyWXIjk2znKrIb8/P3sZOULl067SeRTk4f/YuO3rZxZdw1Tlhe6tDpdCkGHQkPD6dft96UfeHKozsPMGEmazYfnJ2dKaz15eLFi1R/zc1rQu7du0d2jTPqV2b3y3r7c/zYcRp+bhsxdPmiRWxetoKcTi48iHxJySqVGTB8WIZc9t7yYoud/yinTp2igCKHrR5KMkqrCqRBD1eyet5S/FUeBOtDKV7lYwaPHs5Xfboy+dvxtPRqGqeHG59up/NPXTNt/FqtlrYd29O2Y/sU6wZuC6SEsqSNHrqoXHALS5seZiexHuY0vws9PE4X34I2Ze4aJ0REGvTwm96UCs2SSA8LOfilSQ/9VK6o5LZpGkp55OT0keM0elUPFy5m09KV5NC68iAqnFLVPuWHEXY9zGR6JVGWOPTpW0CSpORCUgshxI9vbTDvGVWrVmXr1i106NCBR48eY+vqGvt9STh5FrtXND5okcUi6NmzZ6r6u3jxIkqlCttVVAtGo5mgoFvER7iVEZtX1IpgxIhReHl50alTJ4xGY0zgotSbGD169KBBgwZs3rwZIQSNGzcmZ86cKR9o573lgzNA1Wo1BkyJyg3CjNoh+UAJAEcOHyZw3lqGFWyCUiZHCMGOUyeZNWU6fQZ+T/dxvZg7cTa6x9EonVR0/t9X1K5b+02cSoo4OKgJFbpE5UaMKQaFiMXb25tw2bNE5WGyZ/hmL5jEEW+OrH6+PLoXiqtD/AqKyWJGhylV5zOk9w/ki/BBZ47AU+mKRVgIuf8YdR41IaYIvL29U2wDrCkYQqIjE5U/eBlG1hK2ATz27d3LidXrmPRpdRQxn5fV58+yYPZsvumV1D1CyljTDnwwM/l23iBqtRqDMCYqNwhjKvXwCL/PXceQfM1QyBQIIdh54kS8Ho7vxdwJs4kO1qFyUtF5RJd3pocqBzXhlsTBiYwY0qSHL6Qk9FB6RrnshTI8xrTg45+NR49CcXtVD0Ua9DDSB52IxlMVo4cPHqPOrSbEHJ4mPXyqj0hU/igilKz+tr8R+wL3cmzFBn4qVTdOD9edPMWvs+bQrfe3qervVex6GI8kSdOFEL2FEEcTlFUAugDVgNzvYFiJfyxBC3wFeAD/SQP04cOHHDt2DC8vLypVqpTuvYw1a9bkzp071K9fnz//PI5er8dkijU6Y91h5dgahPFusjKZDD+/1KWUzZ8/PyaTkfjVzdiVztgAQa9G2rXWK1iwIMWLF6dNmzZERekTvJ7QGBY4Omr48ssvX9t/jhw5kkwRY+e/yQfngluqVCnuSE94EhUaVxZpjOZv/Q1q1ko5INtvC5fTLEc5lDLr7JMkSXyWozT7t+1GCEGNWjVYt2cDW4/vYOO+zTRo1OCNnUtKNGrekAuyPzFZ4m8wH0XfQ+5jSXWQkbx58+KcV8W1iHNxbl2Po+/zwOUateskfSMphGDd6nW0qNOSBp82YvSQ0Tx9+jTD59O6S0fWPzhNuD4KALPFwoabJ6jTtNFrxf3x48eMHjyExlWqc+T3PWRz9ORs+ANuR4Qgk2Q4yzUcCTpHhLuCwoULp2ocbm5u5CtTgl03L8Zdk+CIF+wMuU2Tli1s6q5fspQORUqgSPB5aVawKHs2pzp9WZIIkfqHHTuvo1SpUtyVh/A0OqEeRnHKdD3Vetg0WwUUMutcpiRJ1PUvy76tCfQwcAPb/trOxv2baPD5O9TDZg05afkbYwI9fBB5H+El0qSHmtwqLr84H/fdfxR1n7uOyevh+jXraPVZCz6v3IAfh2aOHrbq3JHf/jlHuC5eD9de/5vaTT9PVg9HDRrK55Vrcnh3INkcPbnw8h53IoOteijTcPTmOaI80qiHZYuz5278NQmJDOP30Os0bdXcpu66Jctom6+MjR42zluCPZvsephJ1JUkqZkkSVklSeonSdIZYADWaLupi06VycS4/U4WQkzGmodTA3QG1vBuDOI3ihCCgQMHkidPHrp06ULDhg0JCMjNzZs3kz0uPDwckynx4ghYI+Pu2bOH33/fzdChQ1AqFSROExsbpEiGdW1JgSTJqVSpEkql8tUmk6RYsWKULl06xkskYf7RhBF2E/ZjjbZ79+4/fPPNN0RF6RLUid8bKpOBo6MDNWvWpG3blL1q7HwYfHAroAqFgp9nT2JIjwHkeOaBEhk3TI8YMG4Ybm5uKR7/IuwFbu62ezrlMhkyi1V4Yt270hpB8E2QO3duvhrekXnj5uJnyo1OFkW0Rxgz5v+SpvFNXzSVkT/8yNrjM5FLSrzyuDF78rTXunhNmzCNM6suUt+1GQ5KB67svshXf3Zl1Y6VGYqIWKhQIbr/NJipP09CpbcQaTFSq0kDun2X9Mz5ixcv6NW+E8298/BFiWoEOd5m192zFPLOz8aQC0TfiyDapIdsjmzYvD1N12ToTz8yecxYBhzaiUauRObiyPDpk8maNavtGMLCyJLT1rVPJVeAOSMBXzM3752dDxeFQsHYORMZ0n0A/mFeKJBz0/yAH8YPTZUehoWG4eaclB5K/0o9/HJwJ36d8Cs5RQDRUjQv3cKZNi+NerjwF0YN/JHlf81CISnxyOXOzEnTX6uH0ydN48r6M7TxrofGw4Hzh6/wzV9fsXzrqozr4Y9DmDh2EiqDmUizkZqNG752JfHFixd82+5Lmrh9RMOPahOkuE3gg4sU8s7HlqdniXoQSbRJj5RNy4Zf06aHw34ezaQxYxl2YDMOMiVyNy3/mzkpkR6GhYbh7mm7B9iqhxkJY2vXwwTUA4YDS4EwoLUQ4o93OySQJCkL0A9oi3VspYQQockf9X6ydetWZs2ahU5nRKczAYKIiCgaNGjA5cuXE32vtm3bxp07d6hduzZKpZKuXbsyceLEREajJElUrFiRihUrcvHiRTZv3ozZnDAybqxbrHUlVC63ptL69ddfkx2v0Whk9+7dPH78mAoVKrBz5042bNiAWq3AYDAgSXLit8gL5HJrOiMh4ie5oqJ0REVF8eqeT5CjUsnp2bMnzZo1o3z58v+K3wI7/w4+OAM0NDSUE8dOUKp8Bdx9XVAoFbg/eMKVC5fImz9vXJL011GxZhWOb7tA9Zwl4sruhT/BO2e2f2W46KbNm1CnXm3OnTuHk5MTRYoUSfM4XVxcmDJ3Inq9HrPZnOxNU0REBHvW7aVjlm/i9kkVditGaOhztm3eTss2LV577Ot4+PAh2zZuIexpKJVqV2XNri1ERUWh0WiSjbK4ad16qrr4UMY/AIFAo1LSteDHDDmxl2YFm3Au5CJXw+/QqU+PNOWYioqKYuf2HchkEs27daVGzRqvdVerUK0ah06eo1a+j+LKbjwJwS9PntRfgFcQAiz//Zl8O2+BWD0sU7E8rj6uKJQKvB7k5MrF1Onhp7WqcnzzJapnLxVXdj885F+rh02aN6F2Jujh5Dmp18N96/fQK1uHOD0s4VmYZ8FhbN+yjRatW6b5HOL08NlzKtWqxuqdqdTDteuppPGnVLbcVj1UKvkybwX+d34HLQo15lzwJa6E3+bLvunUQ0lGix5dktXDijWqceToZarnindXDnoWjF+e9AcasethPEKIIKCjJEm9sOYfnSBJkgVYDKwRQry9BLkxSJI0EWvuz/lAUSFEYn/t/xAzZswgMjKaeGPMOhl37949rly5QqFC8Z/9I0eO0KpVK0aPHo3RaMFo1DN//gIiIyOTNRyXLVtGmTJluHLlKrbut1aDVKGQM3ToUPr27Yurq+tr27l+/TqVK1chKioKo9GI0WjAzc2NX375hQsXLuDv70/u3LkJDn6CEODkpMLVVcODBy9eaUlCobAaqq/Gc9NqtYwfPz7Vq7B2Phz+fXcIb5Dr16/TqnZ7Tky+jW6rC4tHL2fHT2vx+BNerL9Nt8adOXr4SLJttO3UgVPqJ2y9/Rc3nz/kj3sXWPTwMP1HZUpaqzeCo6MjFSpUoFixYhm6KVSr1SnO2D948AAvKWvioEXKnFw7dzXNfR49fITuTb4kYtNNPP7Us3zATAZ9NwBHR8cUQ/xfv3CBgh4+AEhIZM3mw3NDOOH6F+y6sYlCLgYa5srG7wuXsH7Nb6kaz5MnT+jUpDl3Vm6l4O0X3P9tJ706dubZs8T7wgDaf9WFvRGhrL98nutPgtl9/Spzrl+kz9AhabsQr2C2SKl+2LGTFNevX6dt3XZcmX0DRaCW5T8vZdfYNXj9bSZi0026N/kyFXrYnrOaYLbd/ZOboQ/448F5loQcZMDojH2+3yRvWw99lV6J9DDAwZ9r59Onhz2/6Ihhx1X8TkewevAvDPquf6r08NqFixRw9wXi9fCZ8QXhunB2XN1KXjXU9c3JzvnLWL869XrYoXELri38nYBLem4u3UfP9l+9Vg87fNWZg4YQNt04Q9CzxwTeusiCf07Td7hdDzMTIUS4EGK+EOITrHst8wPn39FwvgeyAcOAh5Ikhcc8XkqSlAlhkP9dvHjxqnEG1hVJOS9f2tr/Y8aMecVtVSI6WseKFSsICwt7bR9arZbVq1cniJAbG5BIoFAouHLlCiNHjkzW+ARo3LgxISFPefkyAp0uGrPZzLNnzwkODiZ//vzkyZOXQYMGERCQE61WTXS0nvIV8pJYNgUymQyt1iFBOhULWq2aCRMm2I1PO0nyQRmgo/r/RBVjC0q4VMRoMVBA4UdL18/wIQufZC1BN79mTBg2NtmQ/E5OTixcu4Ki3RpwMbcJbaNiLNi06rURFM1mM+fPn+f8+fOpDvWfGdy7d48TJ068RgzfHL6+vjwVIYnSADw03idfUdstKHq9nlOnTnHt2rW4+haLhfPnz3Pu3DmMRiMTh/1MzxxNKO9TjEKeeWifqy5RZ4I5evQoKZGnYCGuPw+Oe+7s7IwliyNGyUDtfHlw9XLjo4AAhn5chTVz5qUqJcCcyb/QNEtuGucvSeGs/jTJX5KGrjmY98v0JOtrNBq+HTwIecWPOeqqQVmzEvPW/Ubu3Bnb+mLf82Qno/z4wxgaKprycZbyVj1U+tHWow6+kjvlfYrRM0cTJg77OUU9XLRuOSV61uNKPh0uTQqxcPNKux7G4Ovry2Pj00R6+I/uIfkK216j1OjhpBE/0S9/Ayr5F6aIdwBd8tfEcOFhqvQwb6GC3AiLz8Hq7OwMWbQYMVA9VwFcvNwoEJCHH4rXYtWsX1Olh7Mn/UJ9zUc0yFWWgp45aJirLHXU+ZgzdUaS9TUaDb2GDERdvRQnfORo61fg1w1r7Hr4BhFCXBZCDADyvqP+ZUIIjRDCWQjhkuDhLIRweRdjepO0aNEixjC0zWkJULJkSZu616/f4NW8nSChVCp5+PBhsv0UL16c0aNH4+CgRKvV4OTkhEajZePGjeTNm/JbHRQUxJ07dxHCgm2e0dhxy3n06DFDhgyhb9++qNVq5HIZXbpWw8Ehdn9nrPuvmapVq9KgQQPy5s2Nj48XFSuWZ/369XTtmnlRz+38t/hgXHB1Oh3P7oXhqbauiN1+eZFyzoVwkDny8kU4Hh5ZcFJq8TK7cvv2bfIk4yLp4OBAky+a0uSLpsn2ee7cOUb0GkZWoycCQYjqGT/NGkuRIkWSPS4jREVF0bv7DwSdC0Ft8SBSdo8WHevRq0/3t+J77+LiQpXPK7Nn/XaqutVCJVNz4+VVbjhfYXTjYXH1du/Yzcwx08gpZSMaHdHuJr7u341po6bgY3JHhsR1/V18cMVJZbvKUMalAId276dSpUrJjqVpy+Z0Xb8B38euFMnqxz9hz+i/ezXZNA4suXCUvO6uaFUOPNGbyO2bk1OnTlGtWtI5VWM5f+IkrUrVsin72D83m48FJqq7L3AvM8eMo4DWk0izgedqwZc90+belhSCD/NGyk7modPpCL0fhrebdY/ejbDLlHUpgEau5eWLl1Y9VGnxFi6p1MMmNPmiSbJ9njt3jpHfDSWbxR2B4JE8jB9njHvjeti3+0DunH2Co8WLUPldmnasy7dvUQ8rNazC5m17+CxrVdRyFVdCg7igDGJg4/jgn7t37mbWmKnkVvgSJXREulro+n13Zvw4mWxmVyQkruru4Sdzxlllu9f0kyz5+OP31OnhV2s34hviSiGv7NwPf8agfcvxVTmy/Mohcru4o1U58MxoJCBbjlTp4bm/TtMofyObstI+efnx6NZEdfcF7mX66AnkU3sTaTbwwtFE52+72/XwLSGEyEjgATuppHv37ixdupQ7d+4SGRmFXC5HpVKxYMECVCqVTd2yZctw9+4/r7QgMJvN5MqVK8W++vfvT6tWrdi5cycODg40atQoxb37ly5dYsSIERw5cgS9/tUsCRZsI95KREVF07dvX0wmC2q1glkz9rB+Ux/691vJlcsPkSTw9PTm6NGjREZGI0kSarWKevXq8dlnn6V4DnY+XD4YA1ShUGDCFBcYQ63QEGmOxCLMyOXxC8GRlmicnJySaSl1REdHM7TbYNq7NMPNzeoG8VwXyqBvfmDjgc2JhCizGDNyAsEn3CnkYo3IKISFDQvWU7jYR9SokXJOt8yg/9DvWem/kg3LVqGP1lOiYnEWDJkfd13v37/PnJHT6e7TJi7/4M2wO/Rs+Q0/ftwXD407AHfC7zPvzDIseS02rnIRpmhc3FJOEeDu7s4vSxYye9IUlp8M5NadIHp/UprNFy7xa51quKrVPIiIwISc/n/8SYtUBARRqFXojEa0qvg0B1FGAw6vBCB59OgRc0ePY3ix2miV1rq3Q0MY3LMPy7esz/DNryXRrKkdO6nHqodGGz2MMEUn0sMIsy7T9HB4j4F8nbUh7g7WRY9n0WEM6T6A9fu2vDE9/HnkBCL/8qKCkzX6rkVY2PnrGgq9RT3sN6Q/q/xWsnjlRvTReop9XIJ5gxbY6OHcUdP4LmeLOD28EXqXb1t1Zfyn3+KpterhrbD7zD65CkshWz18aYjG2c0jxXG4u7szbdkCZk2cwurzO7h56ybdildk27WzTK9QH1eVmkdRLzHJZAw7tZ82qdBDpVqF3mREo4zXw2ijAQdtYj2c/b8JDMrfMK7u3RfBDOzel5Xb1tn10M5/BicnJ/7++29WrVrFjh078PPzo1u3bklGlR4xYgQ7duwgfiXRmqpkwIABidz7nzx5wtmzZ/H396dgwfj0Rv7+/nz9depyzV+6dIlPPvmEyMjoGC+L2LyeCUn83GQyA3L0ejP7913h4IEr5M/vg9ZRTbWq1Thw4I+Yfa8WhACdLprhw4fTpEkTm7HasZOQD8YFV6FQULF2Oc5H/glAEbfy7A87xXPzU7J4Wmdgr4TexCXAI1HkvvRw+PBhClgCcFPH++BncXAnrzknx44dy3D7SWGxWDi09zj+zmXiyiRJRg5lNZYvWPtG+kwKmUxG+y/bs+nABnYe387P036yuabbNm6lvLpk3M0WQIQpimKKvDjL4kU3l4s/yGWcvH8xrizapOdA+Fkat/wiVWPx9/fn51+mMHPVMkrnz4tcq6VaDj8clQqMZhNOSgVYTBTydEvkJpcUDVs1Z931M3F1hRCsvXaaBq1tgyvt2rqdGh554oxPgAB3b1yjLdy4cSNVY38taXA3s68M2EkKhUJB+ZqfcDLsLwBKe5Zj7/PTPDU+i9PDy89v4RzgmWl6WFieI874BPDQuPGR5PdG9fBI4AnyOn4cVyaTZHykqMnKBeveSJ9JIZPJaPdle9bv3cS2ozv5aerPNtd0+8atVNYWs9VDYxQlHXLjIo/Xw9xu/giZnL/uXo4rizbq2RN6kcYtUq+HY6dNYdbqpZTKmx+5o4bKPjlwVCgwWkw4KpRgNvGRm0eq9LBRmy/YdOcvGz3cdOc4jVo3s6m3c+t2qrgUsDFUc7pmxSUSux7a+c+h0Wjo0qULGzduZMaMGa9NaVS4cGEOHz6Ms7Mzzs5acucOYOrUqXz11VfMmTOHmTNncvfuXb7//nty5MhB8+YtKFOmDBUqVOD58+dpHteQIUNijE+JxC63sVsiXv2SCOTy2D2mElFRJiIijJw+fZfKlaqRP3/BOOMzYWoWi0XQuHHjNI/RzofDB7MCCjBo5ACGhA1ny4n5uOJBtIeJ5WI3xcIL8DI0Em1ON8bPmJQpfel0OlQi8ay+WqiIjo7OlD5eRQgBFlmi2WSl3IGIqDfTZ3qIehmJRmabKF1vNqCVOSTaF/apfzn2SOc5efcmLnIt9y3P6P3j96nO2xdLdHQ0WoUSvcmMo0KBTAKZTI5KWJBJAlcHNXq9PsV2Wrdvx5R79xkWuJtcTu7cjgilfN2atGjdyvYcIyNxUSTeeK+RKzP8/ifMzmXHTnr54X8/MOzFMJb/vQh3WRai3M0sNgRSPDQ/4U+j0GR3Y/z0yZnSl06nw0Ek/j6oRca/D69DCAEisR6qZBqi/0V6GPkyEo3CVg8NJiOOSehh5Zxl2G66yvFrd3FRarhnDOXbkf3TrYcGsxlHpRKZJCFDhklmQQY4qx1SrYcP/3nAmN83kUPjwd3oZ1SsV4MWbWz1MDoiEq088e+hRpbx30O7Htp5nylZsiQvXryI23O9ePFi8ubNiyTJEULQr18/JEnCYDDHpXU5deo0bdu2ZdeuXWnq6/jx469MwsiwBkgCb29vgoODsVjMxK/IWpDLZTH7RGPdcq04OjrSvn17Ll26hCSRwKiNb/vOnTtcu3aNAgUKpP3C2PnP80EZoBqNhqnzJhESEsLTp08JCAhAoVAQFBSEi4sLfn5+mdbXJ598wgIxj0qW8shjkm4bLUYuW27wuWtLRg0cTXhYONUbVqNO3TopRjBMDovFwv79B9i1fg+66DBuRh4lT5aKca8/iDpJmya1kmnh7VKjfi0mbxlLYVGAKFM0Rx/+xfXQIM6GXaauqIEDVvcts8XMDekeS9avwGg0Eh4eTrZs2di5dTsDun2Hbw5/mrdvRbZs2diz+3f++D0QR2dnmrRpmWjGMWfOnDwymajp5Miyc+epnycXkiTx0mDCRaPlrweP6OiScjwEmUxG/2FDeNGrJw8ePMDf3x+XJI6rVqcWU7fupaxf3rgb4Ai9jpu6UJsw7OlF2F3O7GQQjUbD5DmT35oeLjbMoaqlLIpYPTSbOG+8zWeurvw4eBThYeFUrV89U/TwwP4D7Nqwh8jo51wyHqZwlvj9kbd0f9GwSc0Mn1NmUaN+LX7ZPoainvmIMuk4/M/fXH0WxOlnV6hvrhqnhyaLmWuWhyxem1gP+3f7jmyv6OGh3VY9bNo2aT0MthiopHFi9aPT1Pa36lSE3oiTRsup4Pt0TaUeDhg+mBe9eySvh3VrMWnTEEr75o/XQ0M0t43P7Hpox04MDx48oEePHuh0RsAUU2oifrUSQMJgMLF//wGePXuGh0fK7vex+Pn5ERLylFfdbJVKFUFBQTx+/Jj58+ejUqlQqeSYTAKZTIHRaCChu65cLkMulxESEkLz5s0ZO3ZsErl4JRwcNNy+fdtugNpJkg/KAI3F29vbJk/Zm/BR9/b2ptW3bfl1xnLKKIsjEJw0nqNQpaKM7T6JT+SV8Vf4sun4Trav28msxTPSlRJACMHgvsO4E/iUgoqPqYEfux6uISTiEjndKxIhu0XWomZapyP/5puiePHiFPrs/+ydd1gURx+A371+R5MqICAgomDDEms0ii2xa+wl0diiscYWa+y9mxiNJbHGbjRGTWxEE1tssSs2QOko9YA77vb7AwROQMGWfMm9z3OPubnZ2VkC7+3Mzvx+lfhm7yYePbxPA3s/Gtp5U9nRmWlnFvCBV2Mc1XacSbtC+4Fdsv9f2djY0LdTT/zTHKhTzJuIkFgG/9QH6xLFcE8WaeBSiuTYZOYM+Jy2Q/rStn3OMjCJRMKY6TMY3fsTXGQyhh09QQOPEmSIcDIyhoolPXnypPB5sW1sbJ4b4rxcuXL4N63L/F8OUdfek5QMHUfi7jF8+sTXEpLcvJTMzOvibfmww2fdWfb1Zmpr/DEickp7Hb/aFVk0dBaNbGrgLS/B4Zm7OLBzH0vXfP3SPhw3fAJhR2OoKK9OM7kLex9t5WHqZcpav0esLJhiFfV0/of5sEzTyizbt5WwsPs0dCxLI3svKju6MOH3ZbT0CcTJwpY/kq/z4afd8vjQT+vIu8W8ibgfl+lDV1tc4yXUcfIlJTyNmf1G0n5Yb9p2MPXhF7OmMbJnX5ykcsaePUxdZw8MiJyOi6Scm/dr92H5ZnVYsv9natv4kGLQcTzxFp/PnGD2oRkzWfz4448IQuZTSVPyahfBlgAAIABJREFUTrDIZFISExOLNACdOHEi3bv3yJX6RUStVvLRRz3QaDR4e3sze/ZsgoKCKFmyJMHBd7OeiEp5Gu1WEAQMBpHExCRGjBiJVCrB2tqa+PhnI4yLpKenv9Egc2b+v/lPDkALS2RkJJcvX8bBwYGAgIAi3xB1/bgrtd+rzaH9vwIwr3FPhnQbRlfrfsglmV+6LuoSHPjrR06cOMF7771XqHajoqL466+/sLe3RyqVcvvoQ5padc/8UA39rMazIXY21ToJ1GvQg3ffffcflRReEAS+mDyWkTFDqX7JgapOvtgUs0Emk1HGxZPvY/+gXedOzG/W3yQS3JaNm6mUVpyG7pl7XEtYOaJNT+Xc1dP0bNYlu14ZRxcmLl3B+82bo84VHKhqtWqMW7iIZaNHUaZ4cUIMIm62xRhf7R2WXbxM6dKmaWJelc/HjuF665b8dvgoVlaWrGg+o8AE7UXFvOTMzNvmlX34UTdq16vDof2/ADC7cX8+/3gIQzy6oJBm+tDNyoVttw6+kg/vBoXRyqZb5ocaGFxhHN9GzqVU1wx6Nuj6z/Thl+MYGT2Ud9QOVCte2sSHa6JO0a5ddeY2G2Tqww2bqZjqnO1DVytHtLpUzvz1J90a98iu52tfgqlLVvJ+i7w+nLBkPouHj8XH0pkwUaSEtS2jKtdi1c0zr92HI8aN5nqbFvx26Cj21pasbD7J7EMzZnJhMBgwGp+dTRHIiU77FBFra2tKlixZpPbbtm3L3LlzGDduHAaDAYPBQNeuXVi61DSNnF6vJzQ0lPwGvqL41J3GrDZEdLoEQEQQxOzJII1GRYcO7XFzcytSH838dzAPQPNBFEWWzF3K4S1BlMzwIUkWT2rxRL5ev6zIX5ienp70HZgZoezatWs4GVyzB59PKS34cfLo6RfecImiyNL5X3Fg4xFK6HzRyhO4q7tKxYz3IFfANLlUjp9VNRo0qkudOnUKbvBvRBAEQoLv09WvhUmSdl9HD5RxZ+k7sH+eY84c/YOOjtVMyiKSoqlu64ZRNGa3o5DK8LG0Izg4mIoVK5rUDwwMZGeFipRMT+WDMqXJMBjYdvUG7pWr4O7u/tqv09/f/7UsMcuNCOT5jjJj5g0hiiJL5y3ht+1H8ZF4Ei9JItE2haXff/WSPsz827527RpuUqfswedTKmh8OH3sZKF8uGz+V/yy+QieGaVJkiVwO+0qVY3vgVVOPblUTgWbKtRv9O4/2oehwffo4d/M1IdO7qhi/szXh6eP/UEnx3dMyh4lRVPF2gOjMSdSrlwqo5TaoUAf7ggoh3uCkSZe/uiNBn4MvkTJdwLMPjRj5i3TqlUrxoz5gszf6qeDv8wBqEIhQ6fTIZVKUSpVrFq16qUm0j777DP69u3Lw4cPcXR0zMwH/AwSiSSfVQVPCwQylwUL5AyKM/OCSqUCFhYWWFtb06hRo6wJS0cqVarIzJkzqVGjRpH7a+bfyz9nGvgfxO+//87JjefoZNmH2raBNLVqR0D0u0wYPumV2rW1tSWJvInQE4zxOLo4YDQaSU1NJb/og0ajkePHj3Ps+7O0Vn5KdetG1Fd/SK3U1pyJPpJTUcysmyx5UqT8as8795uimG0x4lJNk52nZqQjVeY/L2Jf3IG41HiTMkulBdHpyVnLVnKITUvJ9/oFQWDRypUo6wcy6cJlpl+7RakOHZk0a9YrXk3heF0/Z6NY+NerIAhCB0EQrgmCYBQEodpz6hUTBGGHIAg3BUG4IQhCrVyfDRYE4VZWO3OzyroJgnAp18soCELAq/XWzJvg999/5/y2M/R17kHD4u/xoWML6qa8w6QRE1+pXVtbW+IzEvOUP9Yl4uDiWCgfnthwhs6WfbM83ZYGGa04GXU0T90kIf6f70M7W+K0SSZlqXodUmX+S1QdijsQqzX1obXCgrh8fBinL9iHi1etwOaDOsy6c5JFoefx696GL2fPfMWrKRxmH5p9aCYHT09Ppk+fhlqtQCaTIJUKqNVKPv/8c8aN+4K6devw0UfdWbJkCXPnzsXV1ZXmzZtz/vz5Ip1HoVDg7e2d7+ATQCqVUrt2rayUXLn/aIRc758dPmQOWi9cuMCsWbPYunUr589fJC7uCUePBhEYGMipU6eK1E8z/27MT0Dz4cdNe3lHWddkJtrT0odTN4+QnJz80nnxXF1dcfSz59q1y5SzzpyJTtDFc0V+DvvHVtSr0RwyFFgUkzJu8nDee68uGRkZLJyzlAO7DvMoJJzaupakeqRhYZH5yLOMY0UOxm4mXPsAqwx7oiKiCdfd54LiFMcO/UaZMmVeOEu2ecMW1n69CSFdjkRj4LNRfWnVpsVLXWNR6Na/FxsnLaW/b1PkUhlG0cjO+ydp0zP//Vld+37MzH5j+cyqJSqZElEU0Qp6TiZH0DIlEUeLzOAXZx/dQ+XmVODSD6VSSe/+/endP+9ThTeF0WhkxdJlHNq9B7VEChZqhk4YR42aNV+qvbc44X8VaAesfEG9JcBBURTbC4KgIOuZvCAIDYDWQEVRFNMFQXACEEVxE7Apq04FYI8oipfe0DWYeQV++mEP9axqmfjQx8abw7dPvLIPbX2duBh2ncoOmU/FnqQlcEp3hbpxxWlcvSVSgxJlMQmjJg+lXpYPF81ZysFdhwkPfUSg2II0eRqaLB/6O1Vgb+QWwlIeUMxoT1R4NGHp9zkrKZoP13y9EdIVSDUGBr0lH3bt14tNk5cwoGzjbB9uv3+S1j065F8/y4cDrVrl+FCi46QulGapCThoMvdknou4g9rD4fk+/LQfvT8tXC7B10GmD7/i112ZPhQs1AydONbsQ8w+/K+yf/9+Fi5cSHR0DN27d8fOzg6VSsWHH35IhQoVAPjyyy/ZvXs33bt3z97HGRFxkKCgII4cOULNl/z7yY9NmzZRv359IiMjMRrBYMjMG52ers+qkfspbeZ7g8GAnZ0dI0eOQqtNJ3fgJK02jTFjxnD8+PHX1kcz/9+YB6D5kKHPQCJI85RLkOQJi19U5i2fw8QRk9h44RRKQY3RRk9A1cr8suk2vpreSBQy0hOT+GLQXNZstWfnD3u4sj2O+haDOSasR51RjIch4Xj5eKBQKJBKJbh7uPOHZicRl5+gFiyxUFvTx20yv31zArnsO/oO7F1gf37ctYeNM/fzrrofcqkCnTaNZeM2UMzOhnr16hZ43OugQcNAoiOjmLHiOxxkVsTpk2j8YQu6fdwj3/oVK1ak58RBLJi7FFs0PNEnU7NpPZa0GMr8iVPQpBvRZuhw8fNh9twlb7TvReXrRYtJCDrNvFqNkEmkxGmTmTdmPNPXrMTHx6dIbb3NtAOiKN4AnpsoXhAEa6Ae0DPrGB2gy/p4ADBbFMX0rM+i82miC/DDa+u0mddKhj4DaT6DNgnCK/tw9rJ5TB41keN/bUQtUaGzNFKhXlVOb7hPQ81nSKUytE8SmfLZApZts2fXD3u4uf0xH1gM4VdhHRp9jg/lCgUSqQQ3DzeOKncTdeUxKqxQq6z52G0Kx745gUz2Hf2e48Pdu/bw/az9VFP3QyZToEtNY/Fb9GFMZBTTVn6Hg8yCOF1Klg8/yrf+Ux8unLuUYqKG+Ixkajapx9KWQ5k3YSqqVEg1pOPqX4o58xa/0b4Xla8XLubx0bPMrvp+tg8XjprAjO9WmH1o9uG/isePH7NmzRpOnTpFuXLl+PTTT/NEGI+KiqJ3795Z+TTh9u3bODjYc/nyZZOVC6IoMnTo0KzBp+ngbuTIkfz++++vrd+urq7cvHmT48ePExISQrVq1ZBIJHzwwQeEhISQExX36VNRI46OjgDExcXm06LAxYvmORUzOQhvc4nR26RatWriuXPnXurYw4cO8/2ILbxv0y77iyZcG8bVkqdYt/O719K/xMRE0tLScHBwoF6N5vjSG4kkZz7gsfYBrrVDuXb2Ng0kgxAEgfuJf3H/4QVq8AFqewnOrsWJSn3ITc8gnEs4YzxanBIab9QyCwAyjHp+li7n1zP7CvzCbBXYgUpPuqCSWuT0TRfHI58jbNy15rVc64vQ6/XExcVha2uLUql8YX2j0Uh0dDQ2NjbZQTVEUSQmJgaVSpVvGoCnnDlzhu3fbSD+8RPebRxIx25d0Gg0BdZ/HWRkZNCxYRPm125s8hTpUngY19xsGT99GgCCIJwXRbHAZV1PcZGWEnup5xT6/LNSOhSq3echCEIQMFIUxTx/VFlLxb4FrgOVgPPAUFEUUwRBuATsAd4H0rLa+POZ4+8CrUVRvPoqfTRTMK/qw63jNvChc8tsj4QlPeKkwwXWbv/+tfQvtw8b12hJQ+NnSIUcH0Zo7yHWvcO1szf5QDIYQRC4m/gXdyPOUU/WFKWDBGeX4kRoH3KpxAmcXJ1Jz8eHv0iXc+g5PmwR2IEyTzqjkuX4MEkXR1ypo2z6l/pw29qNmT5s0oBOb8mHHQKbMqfaByY+/CsilJve1oyfPhUw+9DswzdHmTJlxFu3br3x84SGhlKtWjWSk1NITU1DqVSgUMgJCgqiSpUqPHz4kK1btyIIAiNGjCb3E0WlUs7YsWP48ssvs8uSk5OxtbUlIyPv00eNRklKSspr6XdQUBD169fP9zNRFGndujX79x/AYMjILpdKpezatYtmzZpha2tLcnLqM300UrasLzdu3Hit/fk7MPfn+RTW3eY9oPkQ2DAQ7w/c2JW8nnOxJwlKOMBxzc9MXzz1tZ3D2toaJycnMjIyEDNkJoNPAEulE/fvhaLGJvtmydOqIjIbDYfFzVxOOs3J5AOctt7DrGXTCA+NwN3CJ/tmC0AmkYNOisFgKLAf2qR0k8EngJXcjtiouNd2rS9CLpfj7OxcqJstyNwg7+zsbBLRURAEnJycnnuzteOHrawYNYWGKbb0LlaBuB//4NPuPdHpdAUe8zrQarVYyRQmN1sAbja2RIQ9fKk2jUV4AQ6CIJzL9TJZaycIwmFBEK7m82pdyO7IgCrAN6IoVgZSgC9yfWYL1ARGAduEXHf/giDUALTmm61/LoENAynR0JN1UVs5GXGW/dGH2CceZsrCaa/tHLl9KOhlJoNPAFtlcR7cC0VNsWwfeltVRGZpwT7dFi4+OcNvCQcJUu9jxtJpPHppH6aZDD4BLOV2xPwrfbiNr4dP59244nRXvkPk1j/p37XX3+vDULMPMfvw/wKj0cj169d58OBBgXVGjhxJXNwTUlN1gIT09AySklLo27cvy5Yto3Tp0owbNyFrD7TppFh6ejoHDhwwKVOr1ahUqnzOJOLs7PzK11QYBEFg+/bt9OrVE6VSiVKpxM7OjrVr19KqVStkMhlDhw5Fo1GRszheRKNRmwymzZgxL8HNB4lEwtQ5kwnuE8z5c+dxKu7Eu+++i0KhKHQbwcHBbFq7lejwGN57/13atGuV7w2FQqGgmIOatLhEVPKcm4UY7Q1qNAtg+/rdPE5cgb3alfIO9ahXohOn1btxaSqlXYeW2f16590q3Ft3DT+rnEmHJH081k6a5yZ19/BxJfbKIxxUOUtCHmlvU6GOaaTCCxcusHPDdtK0aTRp05TGTZs8dy+VVqvl6NFjJCYkUufd2pQsWZKHDx+ybf0PhNx5QECtKrTv3LHATfAFkZKSwu7tOzn/+xlcS7rR6eNueHh4vPA4nU7HphWrmVyhGTKJhDMPgwmODCM6MZalixczcvToIvUD4ObNm+zcuIn4uDhqN2pI85Yt8/0dsbKyIk0uITEtDetcXx7nw8MICCz6ng2RIue9i33ebJQoio2K3AlTHgIPRVE8k/V+Bzk3XA+BXWLmN+xZQRCMgAMQk/V5Z8zLzf7RSCQSJs+eQnDwq/lwy3dbiImI4d0m79K6XesCfWjpoCIlNgELWU5eyZDU61SrVYlt63fxY9IKHNSuVLKvR2CJzpxQ78K2qYy2HVqb+PDOumuUKaIPS/qU4PHVR9jl8mGE9jYVa+f14Y51O0hLTaNJ2yY0KYQPj2X5sPYzPgy9+4BKNV/Nhxd+P4NLEX248es1jPVti1Qi4Vz4Le5EhhKZEs2SxYsZ9ZI+3L5+M/Gxj6nTpAEtWhXsw3S5hKT0VKyUOYPm85GhBDQpepRMsw/NvG2OHDlC9+7dSUpKxmg04Ovry65du/D29japd/Dgway0KrkHlwKXLl1i9OjRpKXpc5Wb1hMEIc9SXalUyuDBg1myZKlJLk+NRs348eNf81UWjFKpZNWqVSxZsoQnT57g7OyMVJqzbW3KlCno9Xq++uorRFFEqVQybdo0Onfu/Nb6aOafz1t/AioIgrsgCMeyIsNdEwRhaFa5nSAIhwRBCM761zbXMWMFQbiTFTmu6dvqa+nSpencpTOBgYFFutk6euQYA9qN4vGP1theqMbeqX/Ss2Nf0tPT860/fspw7mVsIzblLukZyTxMPEeS5RlOHDyFf0YjPLTVkMRYsSd4GX/G/YSs1GPmLpht0q9e/T/mvsMZriedRZuRRGhKMMeMmxg1dfhz+zpm8nD+ku8iNPk6qRnJ3E/+i2CrQwwZNSC7zvervmN+/+l4XbWlcogne6ds4YshowuMXHj9+nXa1O/IztG/8duUWwxoOYJRw0YzqEMflEGPafCkFOGbL/HJh92Jj4/Pt438SEpKonfHHoRtOkmjJA9sTz9heOe+nD/34ghwYWFhuCttUEhlrDhzkEt3rtHMwZue7gGc+n4HK5d9Xeh+ABzcv5+ZAwZRMeIxbWRW3N64jcG9eqPX6/PUFQSBgWNGMef8Ca5HhROfquXXOzcISomlY7euRTrvU4o44/9GEUUxEggTBKFMVlFDMpefAfwIBAIIguALKIDYrPcSoAOw5S108x/Jf8GHx44cY3jHEcgPq/EPrsQf807Ru1OfAn04Zuow/jBs5JE2GG1GEjeTznDf+gQnDp6kqrERZbRVUcVasiN4KSfj9iKWesycBXNM+vVJ/48JcTjDjSwfhqUEc8K4idEv8OEXk4dzQ7abh8nXSctIJiT5EiGWhxma24fffsecPrNwveCI320fdk3czpghY57rww8bdOTnccf4c+Z1BrcewehhoxnUsQ+a43E0jPcmcsvL+bBPxx6Ebf6DRsnu2J4pmg9LKIohl8pYfX4fF+9ep6GVH53sa3Fi1R5WLlte6H4AHPz5AFP7DKP07XSaaItz5du9fPZxn4J9+MVI5l0O4kZMpg8P3b/G8bRoOnbrkk/rL8bsQzNvi5CQEFq1ak1kZDQpKamkpuq4cuUa9evXz7O6Qq0ueDm7wZB7DyVk/nbmPDF8Gv32WaZNm0b//v1QqxVYWCixtNQwceIEevXq9Touz4SQkBB69eqFu7s7VatWZevWrSae02g0lChRwmTwCZkD5Tlz5vD48WPu3btHTEwMgwYNeu39M/P/zd+xBDcDGCGKoh+Zy1A+EwTBn8wZwiOiKJYGjmS9J+uzzkA5MvdNLBeEfCIE/UMwGo3MnbiY+rKP8bAoi62yOJUtGiHccuKnvfvyPaZ27Vp8v2MhpRo+Jt39ME17F6dG9cqUTmxEVeeG+JUqj0cxX8pLG5Fc/C6bdq/Ns0/Hzs6OjXu/w7e3DZc99iFtGslXO+ZRs+bzZ5TLlCnD93uW49w2hXsl91Gqi8DGvauyc8AlJCSwa/U2PvHoQCmbkpSwdKa92wfEnn/EX3/9lac9URQZ2X8slVM+IEDzHhWL1eYDZR9+Xv8TnWzqUcWpLMUt7Knm4Id/qjPrVj1/T216ejqPHj1Cp9OxZcNmqupdaOpRDRdLe6o4+/KpZ1MWTs6bQiU5OZnw8PDsICn29vZEpyVx93EkiYnx9ClTC08re9wtizG8ciOObd9DbGx+G+fzkpGRweoFixhfox6VXN1xL2ZLl/KVcUtK49Cvv+Z7zHsNGjD+m6WcKqZgecQdxLrvsOKHTc9dIvc8xCK8XgVBENoKgvAQqAX8LAjCL1nlroIg7M9VdTCwSRCEy0AA8DSPw1rAWxCEq2TeWH0s5nyD1SPzScG9V+zm/zP/eh8unLyIzsW64WtTBie1E+/ZB2Ibas++AnxYq3Ytvtk5D3XjCO6U3EdAX1uqVq9EpeRG1HRpiL9PebyK+VJd3oj44nfZWIAPN+39Dv/eNlz32IeyaSRfF9KH6/csp2QbLeEeP+PfWcLmZ3y4Y9V2ujl3wsvaE1cLF1oXb0HUmYgCfTj607HU0TWhhnU9qtrXpoNVL/Zv+ImutnWzffiOQ1nKpxXdh1X0zryf5cOqzqUZ4NWk0D6M0SVyPz6ChMQEunu8h7vGkRJqOwaUac6hLT8VyYcr5y1muN/7lHcqSQlrez70qUHxWCOHfinYhxNWLOGso8C3j28gaVCFlVs2mn1o9uE/nlWrVpGRkUHm7XPmANJohPj4BI4cOWJSN3OgqCT3wFKhkOPn55f1ZPRZDCiVMqysNCxbtizfvMVSqZSFCxcSGxvLlStXiI2N4YsvvnhuYKyX4dGjRwQEBLB+/QYePozgwoVL9O7dm2nTCr/1QqlU4uzs/NxVJ2b+u7z13wpRFCOAiKz/ThIE4QZQgszQ5PWzqq0DgoAxWeVbsqLG3RcE4Q5QHfhHJhSKiopCnmqVZ1+lh7w8x389RfsOH+Z7nK+vLwuWzMh+/36dNtSzaABkrvt393DDHTcOG84XmPbAzs6OYaMGZ+4sKQIeHh5MmTUh38+uXbuGj9wD6TP7dfxlpTj7x2kCAnJSlYmiyLSJ07l36Q4ZUpEjxseUt69NVYdALIxqlDopCenJrL20m7R0LTJBSvDiozRu3jRPcnJRFPn2q+Uc3L6b4kpLotKTSUpPY5xPe5N69mpr9KEppKamolar0el0zP5yMtdOncFepSY6Q8eAMaNo2LgxvjUq88PhP6hpn5mOQGfIIDEjnZJ2JagQ78Tly5cJDAx84c8rNDQUN5UGtdz0KVAtVzdOHj9Bs+bN8z3O39+faQvmvbD9FyGSOWp5G4iiuBvYnU95ONAs1/tLQJ5lbVkRILsX0HYQmYOu/yz/BR9apFugUZkOEP3U/vxx6CQfPseHc5ZMz37fvE4bmltm+VCjxr1kpg8fviEfTn2OD72knkifGfOXkfhw5vczeXw4feI07l26gyg3ss/wmMpOtahdvAGWogpVeqYP11zcTWq6Frkg4XYRfTi+tOnPz15tjT7E1IezJk3h6smz2CksiDGmMfCLETRq0pgyNQPY/ksQla08AdAbDCQZ0yhp54J/qmuRfOgis8rjw2r2npz97Q+atTD7MFe52Yf/54SGhqLT6Xn2+Y0oikRERJiUTZgwgUuXLnH48GFkMgVGYwblypVj4cKFNGnSJCtVydOnoBIUCinbt2+jcePGBez1zEGj0eDl5fVary03vXv3Jj4+AXjqOoGUlDRmz57N8OHDi7xdwIyZZ/lbpyUEQfAEKgNngOJZN2OIohjxND8WmTdjp3Md9jCrLL/2+gH9gELtg3kTWFtbkyomIoqiyYxUoj4Od7fihW6nmJ0N2tBELOXFssvSDamobAoXmOJ1YW9vzxNjPsnijQmUcq5qUrZl4xaC99yhl3VfikkcMYgG9j3ZzQ2ZNXrRQIbRwPJzW2huXwl/a3fSDXrCjHGM6z+cDft3mAhtx5ZtBO85yswqzZEIEjKMBqYd3s6v987S0T/npsgoGkkTM7L3k82fPgP7O2HMrdMYQRDQ6nTMmDkHVzc3JsyYyoDIvly7eA8fG0ekchklSnogk8uI0WtxcHAo1M/E1taW2DRtnvLIpEQcylYsVBuvyr8zdvV/m3+rD5ONyXl8+FgXh3MRfGhjZ0Pyo0Ssc/kwzZCKyvLt+zBeTMhTHi8m4O9SyaRsy6Yt3P3pDv1se2EnzfTh7rg9XJZboReN6I0G1v65lRYO5fG3yfRhSEYc4z8dzvqfC+nDu2fpWO45Ppw2E+trkUwNaJHpQ30686fNp4S7GxNnTmFAVD9unnyIl6UzUoWMEiXdkcnkxBmSi+TDJ7q80TejUxJwdC1aSpWXxexDM2+LRo0asWvX7qyUKU+dJmIwZFC7dm2TugqFgr1793Ljxg0uX76Mj48PVapUQRAEhg0bxqJFi9DpMhAEUKsVTJ48mZYtW771a4LM1SrffvstixcvZsCAAfzyyy/kXSQpIJPJuH79OjVqFH2/thkzufnbouAKgmAJ7ASGiaKYd4STq2o+Zfl+34ii+K0oitVEUaz2NB/R28bCwoJ3GlTiuvZk9lr5xLQnHH+8nYM/HaJb2084dvTYC9v5ZHB3zuv2kmHM3ENjFA1cSNtHj34d32j/n8XX1xeji5TrT25nl0WmxHCFuzR5v4lJ3e3f7eADh+ZI5QIZRh1SQUpDTVP+ijuOYKlg3+PTWAsK/K3dMYpG4vXJlHbzpLrKk18O/GLS1s71G+lSpnp2pESZRMqAdxpz8MFptPo0IPNm66eQ09RuUp+l8xfxYaPm7P5+A+86uvL0XlejUNDJy48dGzaiUChYvvpbYpw0yJxs8SzljUqt4mpUGHEaSXay5xdha2uLWzl/Dt+9nf3/+GZ0JMv+DOLIz7sZ1Ks7f7zGfFzP8jTv3T9lz5OZV+ff7MOAepU4G386+28lXvuEPY92c+jnQ/T8sFehfNhzcHf+SDP14UntPrr+DT4USki4mZCTwiE6NZobstt5fLjju+00d/oAiUKCXtQjFaQ0tWzMn1G/I1jI+enxaawEOf427hhFkXh9Mr7uXtRQleSXAwdN2irIhwcenDHx4d4HZ0x8uHPtRmrYevBUiBq5knYlKrB9/aYsH64k3l2OwsUaT29vVCo1N+MeEG9tLJIP3SuW5bewa9n/j4Pjwvn26i8c3ruHgR99ZPahmX8NHTt2pGRJD1QqBU9/qzQaFR07dkSj0XDhwgW0WtMJaj8/Pzp16kTVqlWzJ+JmzJjByZMnGTt2DC4uLpw7d47RLxH863UxZMgQRowYya1bwVlLjPO/Xt3CAAAgAElEQVRDJDU1DVdX17faNzP/Tv6WJ6CCIMjJvNnaJIrirqziKEEQXLJm+12ApwmaHwLuuQ53A8LfXm+LzuTZE5gmncnBI18hN6i5HvoXlYs1oabYnOS7T5gzaBVR42Po3K3gm6cmTRoTMzaWtcu+QW2wQSvE03Fgazp26fAWryQzYMSiVUv5cuQEjlw/g1yQI3NQsuC7xVhYmC4z1qWmo7JU4e7pRuj9MCQZMkRRIDLjPpOmTuLShXNE7r9EtC4eA0YcXBzRaCywk1kSF2Waj1uflo6F3PTphlMxWxxcnVgYth9ro5J4fQo1mtTl8rkL1MhwZqBLA1aGRpIc8wS9To+Tc+YTFidLKx5HZwYZVCqVzF+zgqmjxqJ/cJ4M0Yi9lwcLV31TpD0Uk2bPYvaXkxn9x2FkItwMucnkFtWp5+9NREIyS2dPIL7fcJq3Kmzk/qIhmuf8/zX82304cdZEZkycyZqjK1GJaq7c+4sGxRvS1PoD4mOe8M3nK4kZHUPHrs/3YewXsXz/1TdY6G1IlsTz4YC/x4eLVy9h0oiJnLh2EpkgQ+GoZOGKRfn4UIfaRoVbSTfC7ochNchAhEf6B0ycM4mLF84RdeAi0enxGAQjDs5OaCw02MZbEBcdY9LW83y4IPQA1kYF8RlaajSuy+U/L1Bd78qA4oEsvxdDcvQT9Ok6imf50NHCmsfRj4BMHy78bjlTRowj/fYpMkQDjqXcWLxgeZF8OHnuDGZNnMKUs3uQGQVuhd5kTN33qFO6NJFJSayaPIUngwfRorXZh2b+v1GpVJw+fZpFixaxZcsWNBoNvXr1Yu/evfj4+KBQKDEYMpgxYwZDhw59blsBAQEEBAQQFBSUZ9n92yQyMpLVq9eQnv7s0uLcgZIyp3q8vLyz98SbMfMqvPUBaFbOqzXADVEUF+b6aC/wMTA76989uco3C4KwEHAFSgNnX3Se+Ph4ls5dgF9ABRoEBr7VTdBKpZLp86eg1Wr5evE3OK8rh59VLQCsFfbUlXVl9ZIVdOj8YZ7oYbnp9lEXOnXtwJMnT7CxsSlS5MmikJaWxi8Hf+XOjbuUC/CjYaOGyOXy7M/t7OxYtnY5SUlJ6PV67Ozs8m2ndAVfHlx5gJeVFz6+pUjRpnAj/jodOrbn4949aNexDd3Ot8XFyw2VQsHThzkXU0IYWO8jk7ZKlfPjRswj/J3csssuRTygXpNGTJgxlbi4OKysrNixfTvhP53D380ZjSgj1QgSZCTFJ2Dv6IBUKuWPhw+o0b5FdjteXl6MnTmVfbt3o1Aoad3+Q4r6hEij0TB13ly0Wi3LFsyjQ6wd9f1LAlDC1opxgf58NGUC9+/cpX6TJpQvX75I7b8I8+3Wv4O36sN5C/Cr9Pf4cOrcTB8uX7Kcsjv8eMe+OgB2KnvaKzqybtkaPuz0fB92/agLHd+iD2/fuEuFAnz41Xdfv9iH5Utz//oDvK29KOXrjVar5dqTG7Rv9yEf9e5B245t6H6hDc5ebqgUyuxn2xeTQxlQt6dJW0Xx4aOfLuDn6oxalKNDRIqM5PhEHLJ8eDryLjW75gRP9vLyYtzsKezb9SNypZI27du9lA+nLZiDVqtl6bz5tLrjSt1SmctvXa1tGF6lOgMmfcn9O/do0LSx2Ydm/q+xsrJi0qRJTJo0CYBmzZoRFHQcnU5PWloGIDJu3Hh8fHxoXkBMiJfl6tWrnDt3Dg8PD+rXr//c9E+F5fLly6hUStLTn33yKQA5kX0lEgnr1q175fOZMQN/zxLcOkAPIFAQhEtZr2Zk3mg1FgQhGGic9R5RFK8B28gMY34Q+EwUxYIziWeRGhOP3elwfluwnk86diMlJe8elTeNRqPhzo0HuCpN98HIJApUGdY8efLkhW3IZDIcHR3f2M1WTEwM7d/vys5xJwhbDxtGHqRzyx4kJuZdBWhlZVXgzRbA8AnDOCoc4lzcn0SmRnI17QpX7C8xfNyw7OO7DOzF8rsHuB4bwr0n4ay7cxi7Kl5UrGi6d3LwmJFsjLjM0QfXCUuI5dD9K+yMu02/oYOQSCQ4Ojry+28nWDR+Lj5Ge8T4DCJDwmnsXIVZF05yNTaa4KgItl27xGWZgdZt22a3vXr5N8we8BmuV29hc+4SY3p8zE8//vhSPz+NRkPY3dsEeOTcsCUlJhH3MAybdC025y/x1ecjWTArb2TKV8G85Oxfw1vxYVrsE5wvPOD3JWvp3bHr3+bDBzcf4GVhmitPLpFjabT6x/iw3ftdWT/uN66vh5UjD9DxJX04bMJwfhWPcDb2HFGpUfyVeoWLtpcZNnZ49vGdB/Ti6zu/cC02hLtPwvn+9lHsqni/nA+Pn2DxhLn4GOwhQU9USDiNXaqw6OZRbjyJ4E5MODuDz3FDnU7rdjk+XPX1N0z9ZBhWfzxCejiYEZ1789OPe3gZNBoNoXfuUMHFJbssKSmR2LAwLFPS0Zy8xtIhY1gw0+xDM6+OIAjzBEG4KQjCZUEQdguCUOzFR71eoqKiOHr0WFZgoqerBgS02lTmzp372s6j1+tp27Yt1avXYNCgIbRu3YYyZcoQHv7qC2A8PDzQ6XTkTOVkBkXKyU0qQa1W8/nnn1OrVq1XPp8ZM/A3DEBFUfxdFEVBFMWKoigGZL32i6IYJ4piQ1EUS2f9+zjXMTNEUSwlimIZURQPFOY8xZQaKrt408WnFlV1tqxbtfbNXdRz8C1Xipj0hyZlRtGAVkjIDjmfkpLClStXiI6ORhRF7t27x40bN7JD5r9J5k9fTKmoulS1aIyXZTmqa5rh8KAcK5atKnJbHh4erN+3Dveertwtf4vSfb3Z8NMGihfPCTbS5aNujFk5iwflZJwrkUClj5vQb+jAPMu93N3dWbXjB6SNAjhikYRFs5qs3vEDzs7OQGY6giWTF9DMuT7RuhTUMjUOClvcBVt6B7Rhc9g9Dsr0eHVtx4oN61GrMxOeh4aGcnzHTibXqce73qVo4FOaaXXeY93iJSQnJ7/Uz9DTpwy3IzNvnkWjSHREBA4qNekZ4GpTjC9q1CL42G9cv379BS0VjqKkHDA/Gfhn87Z8aKvSUMXVix5lq1NDtGLd6jVv7qKeQym/UoRrH5mUGUQDiWLiP8KH86YvpnjUu5SzaIK7pT8VNM1QP/Dnm5f04bqf1uPSw42bfnfw/qQ0G/Y+68PujF4xm/v+cs65JFPxo6b0HfKyPpxPC9d6xOiT0MjUOCqL4UYxelduzY64mxyzTqZMz5as3LTOxIdHf9jLqPLNqelWlroe5RhTrgVr53310j70Kl2aO7GZS4hF0Uh0eCT2Sgt0RiMu1sX4vHI9bh353exDM6+DQ0B5URQrAreBsW+7AzExMSgUcvJuzxdeanB4584dNmzYwJEjR0zyii5evJhffz1Eamo6KSmpJCdruX8/hK5dXy6XeG7Kli1LtWrVsq4j5y9FqVTy0Uc9GDZsCMeOHWPevFePWm3GzFP+E8l53nPzY/Yvhxk4bPBbP3e3np3ptr0vNmkOOKrc0RnTuJD6Mx0GtkKhULBmxRp2rN6Jq9SNsNQQYpMiqeBcFoVEQbTsMRPnT6HaO3miub82Lp66TDML0wTBZSyrceTgt4weP6LI7dnZ2fHp4E+fW6dChQo8jo1lydSZZNx5wOnNu9DbWjLn66U4OTll17Ozs6PfZwPybePKlSt4yUpQx7ka889+jYfGAT8rN+SinKMhl2nSrjVT5s3Mc9wfJ07wnpNLdjAPAKVMRjU7B86dO0f9+vWLfM2dPu7FqN7dKG5jgbOFAr3ewITfzmPUi/x8/k/uJiRQzcuL3w4ffm37PETBfCtl5uWoX7IM0345zMChQ976ubv07MInu3tjn2JPCQs30g3p/Pr4IG0+aY1CoWDtijXsWrsDN5krISlhRCVGUd65LEqJgmjpEyYumPxGfXju1F/UecaH3pbVOHZwFWPetA+nzSDj7n3O/LATva0Vs78qmg9LyVx5t3hV5pxagYfaET8rNxTIORp6mSbtWjE1Hx/+fvwENa1KmvhQIZVT0cL1pX3YuVdPPv/oI5wsrXBSqdFnZDD94gmMGUYOXDrD/eR4qriXIuiQ2YdmXg1RFHMnmj0NtC+o7pvC19c3a8Lo6dPCTGQyKY0bNy50O0ajkd69e7NlyxZkMgWCIGJnZ0dQUBCenp6sWLECrTaN3E9ZDQYjp06d4smTJ9ja2r7Sdezdu5devXqxf/9+BEHA3b0Eq1evpkmTJi8+2IyZl+A/MQDV6tNRZc34vm2cnZ1ZsWUhc75cyMVbP6K0lNN1WHu69ejCb0G/cWjFET5x6IeAwLL78+li/QFugjPOLi4kpCcxefAENv+67aUTdL8IqUyCIT0DmZCzx0lvTEOpfnPpDcLDw/n6yxl8WaUBlorMXFc3oiMY89kQvtu+pVBtaDQaUo1pqGRKBlb5hB9v72fbo7MkZ6RQvvE7zJ01Nf/jLCyIztDnKU8xGPIksy8s7u7ufLlkJcvnzSL0zhWCbwXTwcefSY0qIggCienpDA86Ruumhf8yehHmpWRmXhatTve3+nDJxsUsmLKQ/cH7UKgVdBzegS5ZPjy26hADXXshIDDv3hK6WDbDFReciztn+XAim3/d+sZ8KJNJMaZnIH3Gh6o37MPlk6cztWp9LLPSp1yPiuCLQUNYu63wPtSK6ahkSgZV+5hdtw6yPeI0SXotFRq/w7wCfGhhacEDoy5PuVbUv5IPpy5fzlez5xB67jbBN4Np61WBCVnpJ5J06Yw5c4C2xvov1X5+mH1oBvgE2FrQh7nTUjk6OhIUFPTaTrxmzRrCwsKyV2kIgoBUKsXf37/A8xgMBkRRRCaTkZyczK5du6hYsWLW/uicgeyBAwfw8/Nj6NCh6PV6TJ+0Zqa3OnfunMk+9Zdl6NChDB48mJSUFNavXw/wWn9OL0tycvI/oh9PMffn9fCvH4CKosiO+3/SdnC3N3qeiIgIFs9czJU/ryFXymjXoy09PumBRCKhdOnSrN78TZ5jflj1A/UtGyIVpNxPuoerzIFSGi9iE6JxdnXGRmlFZWkZfj34K+07vpmJvZad3ufUiiCqWWYOjkRR5HzqYTr1avdGzgewf89emjp5ZQ8+AfycXFBcvEVISAglS5Z8YRt+fn7EW6byMCkCNysXPqnYlRS9lhXhW5mzZEGBQVYaBAbSa/ESAlNLYavOvMF6lBDPrTQtk6tWzfeY/DAajfywYQP7t29Fl5ZG6QoVGTt9NklJSYxt9yFd/cplL6OzkCuo7+bGzWtX6dGmBTptCm7ePgwYOQYfn6LnyRMBg3kxmZmXQBRFtgZfoM3Aj15c+RWIiIhg6axFXDt/BblSTqtu7ejR66NsH67YmNeHW1b/QGPb+kgFKXcT7uMsdcBH40lUQizOLpk+DJCUfaM+bNXpfY6uCKJ8Lh9eSz1Mjzfqwz28X7xk9uATwL+4C4qHwUXzoUUqYUmRuFs50zegMyl6LV+F7WD2C3y4dv5X1Evzx0aVGcU3Iukx98R4qr6ED/dt2Y4uLR3fiuUZP2smSUlJjG7VkU6lK2b7UCOXU7d4SW5cu0qPVq1I16bgVsqHgaNGmX1oJg+CIBwGnPP5aLwoinuy6owHMoBNBbUjiuK3wLcAZcqUEV/m6f7zOHr0KHPnziUsLIxGjRoxevRoSpTIm6I5LCyMrl27cvbsWQRBwNvbm/nz5zNr1iwuXLiE6c44EZVKwa1bN9m1axcrV36LTpd7y78RX18fbt68WaSo1S8iKCjopVY/vCnM/Xk+/7T+FJZ/7QA0MjWBTXdPEZwcTZ0WjWnV5s2EfwdISkqiX6f+1EmtzyfW76IzphP09VEiH0Uy5ssxBR6XnJiMhTzzSz/NkIaF5OmATMhezKER1CTlEwDjddHvs76EPpjMgWMrsTW6EicJ472ONenQ6c2tZEmKT8BNqcpTbiVXFnrfkSAILFi1mJH9hmMVrkQlKAgVoxg9b8JzE6hbW1szdt5cJo8dh6/GEp3RwCOjgZlff/XcCJzP8vWiBcSf/I1ZNSuhlsu4/CiSYb0+ZtD4ifh6eRGjS0eWno5EgDSDkWiDnoxbF5nRoh7WKiW3ImMZP6APi9ZtfqmcWuYZfzNFITwlkXU3z3IrKY7azZu8cR8O7NqX96XV+aBEN9INOg6sPU7Uo0hGT/qiwOOSE5OwlGdOCqVmpGEhyXxKm3t1pYWgeqM+/PSzvoQ9mMwfx77FyuhCoiSMwI613rAPE/HI14eKIvlw/rdLGNV/OFZhClQSBaHGaEbPfbEPJy6axYxRE/CS26IzGoiSpTFnxdIi+fCrBYuIPnqKCWXroJLJuRr1iCEffcKQSePw9fQmTp+GVJeGBIF0o4FoUYf+6lUmNW6AlUrF7egYxvbrx5KNG80+NGOCKIqNnve5IAgfAy2AhuLTRLR/A4GBgQQGBj63jsFgoG7dejx8+AiDIfO39saNW9y6dSsr0FneQaRUKkGr1fLll1+yb98+oqNjSUlJQaVSIpNJWb9+/WsdfJox87b41w5A3bw9aT/zc7y8vF55bfyL2Lv7J3yT/Sht5wuAUqqiid0HfP/TtwwYPqDA5WKBLQO5tOICdezr4mXlzcH7e6ifkYJcJUdAQBRFLuluMrNB3zfWd5lMxuxF04mOjubhw4d4eno+N7Lj66Bu44asOzqVKiU8s8uSdWncS03A19e30O14eHiw9cAObt++TVpaGv7+/oVahlLtnXfY9stBrl27hkwmo2zZskUKZZ6SksIf+/ezrMm72eKv5OZC04RE7t66SbA2BedKlRFEI0ajEYVCwZnfj7CtX1usVZlPOco4O9C9rJatG9YxfExR4yaI5rx3ZoqEm7cnbaeNfis+3PfjXgIMpfB3KgWASqakTYlAFh7YTOKwgQX6sEGLhpxbe4n3nOrgY+PF3rSfeS9Di1yVGfFWFEUu6m4yq0GfN9Z3mUzGnL/Bh+uPTaGqm2d2WXJ6OndTE4vswy37X86HO47sfyUfnth3gNnV38/2YQVnNxqkxHPn5i3upifiXO4dBEOWD5UKzp07wKYenbFSZQ68fZ0c6exdkq3r1zH8C7MPzRQOQRDeB8YA74miqP27+/MiDh06xOPHcRgMufeLZv7r4eFBWNgj0tN1uT4TsbKywtfXF4lEwtWrV9m8eTPHjx+ndOnS9OnTB5dcEaeLwvXr19myZQs6nY727dtTrdqb21tvxkx+/GsHoHK5nCpVqryVc925Goyr3M2kTBAEHCVOREZGFnjD1aVHF/ru78evYQfwlvrgZFOCBZFr6VCmGYmxqZxNvUrNdvUoVarUG78GJycnk4AXb5KqVavyc9XyLD7/G/WcSpKYnsYv0fcZOmV8kfcxCIJAmTJlitwHqVSaJ9VBYYmKisLD2iLPrKOfgx2H797l0zFjmDBrNi08PLCSK9kfch9nh2LYWFma1C/rbM/hWzeLfP7MdNBmzBSet+nD4GvBlFSbrpgTBAEXuf0Lfdj/YF9+fvQrPkpvXOxcWRC5hvalm5MYq+XPtCvU+rDuv9SHFVh4/gTvFXcnMT2NA1EhDJ084f/Gh24W1nl8WLqYI6fu3GXAF6OZOnMu7zt7Y6lQ8svDYJztbbGxMPVhGUcHfrt1q8jnN/vwP81XgBI4lPX7d1oUxedH/fobCQkJyX7ymRuj0Yi7uzueno94+PARKSlaFAo5MpmSjRs3Zk8IaTQa+vTpQ58+rzYJt3DhQiZMmIBen4HRKLJs2TL69+/PwoULX3ywGTOviX/tAPRt4lfZn1OHz+KJV3aZUTQSbYx67nIitVrN9zu+4+iRo1w4eYF23m2oVPVLjh/+jbTUVL5o8SUVKlR4G5fwSuh0Oi5evIgoilSpUuWFOfoEQWDSrBmcP3+e44cOY2Vjw+I2U15q6ZXBYODSpUukpaVRuXLlFwbOMBqNXLlyhYSEBAICAp4bzCQiIoLbt2/j7u6Ot3dO7kIXFxceJCZjNIpIJDk3XVeiYyndtDaNmzbFr1w5fv7xR2KSU+g3sD9zxo0mXZ+BSp7zJ3flUQyly7/crKNoXnFj5h+KX4A/V04ep1Qxj+wyo2gkXBf7Qh+u3fY9R48c5eKp87T0bsuEKgH/DR/OfNaH0/+vfBiakoBRNJpE073xOJLS9ZvQ+P2m+JUvx8+79/AwOZkBw3sx+4sxpGXoUeUaYF+LjKZ0+ZcbBJt9+N9EFMWibxr+G3nnnXfISXOS85RTIpEQGBjIN998w9atWzl06BCenp707dsXT0/P19qHsLAwxo8fT1paTkAjrTadlStX0rVrV/OTUDNvDfMA9DXQonVzflj9AzZPilGuWAVSM7QcTThE025NsLS0fO6xMpmMJk2b0KRpTqjrzCho/x+cP3eeKcMn4CNxRgRmGiIYP38KNWrWeO5xgiBQrVq1V5JdcHAw4z4bgo/cEpVEyoLEGPp/MYqmH7yfb/3w8HBGfToAN8BWoWBZbCzt+/ahUzfTAFVGo5FZX07h5okz+Fk5EJKagLKkC3O+WoJarUatVtOkfUcWHfyJTyqXx0al4uT9EI7FJbG6TWaCdzc3N/oPyknn0LXvAGZ+v5xBNcvhaKXhQmgEW+9Gs2zyywWDMZqXnJn5h9K8VQu2rd2MXfQ1Kjv6kaJPZV/kcRq3/+A/4cOpwyfgI3UGUWSmIfKt+nDswKF4S61QCTLmp0Tx6diRz/XhiL6f4ZSuwEaqYlFyOJ0H9KJTd9O8gkajkRkTp3It6BylVS481D3GwseRecsXZfvw/U7t+ebHX+hWpirWKhVnwu7xhzaWtW3bAFk+HPxZdptd+3/KvFXf8mmVSjhYWHDp0SN2PnzEV3NeLs+g2Ydm/h+oUqUK9evX59ixIFJT0wFQKOTI5XI6duyISqWiZ8+e9OzZ87WdMzk5mS1btnDr1i0CAgKIi4tDEKRkxmx6ikBamo6dO3eaB6Bm3hrmAehrQKPRsHrbKlYsXsn6oNVoLDV0GtSJtu3b/N1de6OkpqYyecg4Bri0xkZpBUCiLoVpwyey7ciPhQrjbzQaEQShyJvojUYj4wcNZah3VVytM/e0tdfrmDJrHgFVKpske3/KhGHD6eNeEt+spXWdDQamrP2OSlWrUrZs2ex6u3bsQPvndSZVaZzdr1/vXeOr+QsZNXE8AL0/HcA+1xLM2rCelOQkKtesxbLpCwq8wW7Tvj3WxYoxf+0qEp48pkz5isxfPeOllvmZl5yZ+Sej0WhY8cMavl26gkW/bUVjoaHDiM60+bDt3921N0pqaiqTh47jM9dW2CgzPZCYnsL0zyey9fCb9+G4z4Yx0L06rlaZe1ZT9TpmzJhfoA/HDRlBJ8sKlPLI3EPWxmBgwYqNVKpWxdSH23cSf+Ien/u0y+5XUNhFls1bzOhJmfs1+wz4lH2urixdv5GUpCSq1KrF14OnFujDtu3bY1OsGMtWryb+yWPKVKjIgrXfmX1o5l/P7t27WbBgAatWrUanS6dDhw74+fmhUuUNRPaq3Lt3j5o1a6HVaklJ0WJpaYFSqSA/vUgkwgtXa5gx8zoxD0BfE/b29oyfNu7v7sZb5dSpU/jLPLIHnwDWCgsqyr04ceIETZs2LfDYkJAQ5k6aSsTdBxgEqNu0IUNGjyy0AK9evYqHRJ09+ARQyxU0cizJwZ9/5uNPPjGpHx4ejiwhAd9yOUv45FIprT29+HnXbsqOywl8sW/LDgaXqmRyE9jIy48xhw9C1gBUEARatm5Ny9aFjyYa2KgR1apXZ9HsqVw9d5IRfU5Ryr8yn4+bjKOjY6HbAcxBN8z8o7G3t2fslPF/dzfeKqdOnaKc1D178AlgrbSgotyzUD6cN3kKkffuYwDqNGnMkFFF86EbmuzBJ2T6MNCuFAd/3s/Hn/QyqR8eHg6xKZTyywlgIpdKaergz74dP1J2Qk604j2bd9CzRF0TH9YrUYkZv27NHoAKgkDLNq1pWYToyk99uHDWNK6ePcXwnqfxKR/AiAlfmn1o5l+LQqFg7NixjB2bc8/xpnI49unTh7i4x2SmJ5WQnJxKamoaOVM2OcuA5XI5nTt3fiP9MGMmPwof6s6MmWfQ6XTIxbyh+uWiFJ0ub3LzpyQmJvJ5r340x4lZ77RidtUWyP68y+TRhY9+mJGRgUKS99xKqQx9uj5PuV6vRynLp75Mhi4t1bSuTodCajo3kxmV+NXm2UVRZNSgPlSX32Zjn/Js6F2eZo4RfP7pxxgMhhc3kAtjEV5mzJh58+h0OuRCPj7kxT4c8Ulf2qqKsbBeUxbVbYz6rxtMGVNwyppnKdiHUvTpec+t1+tRSPLOPytlctLT0k3r6vT5+9D4aoM+URQZObAfVdJCWduhBms71KCx+gnD+/Yy+9CMmVdEp9Nx/PhxjM/8nRoMRmQyGSqVHAuL/7V33vE1Xn8cf3+zdyJDQhASEnuUHzVKzdraKmrUaquKtnRSqtVJ6dBaNVqlVVsppbRGdZi1Y69EkEQkIonMe35/3CsSMm5k4rzzel6599wzPnc8n+c5z3PO99jh4GCLnZ01H3/8MdWqVSsmtZoHEd0B1dw1jRs35mDyWZLSbp3gpKSlsj/5NE2bNs223K9r1tLMyZcAd2OkTAuxoIN/bUL2HyEiIsKstmvWrMmxhGiuJ93qPBqUgc0R52n1WNs78leoUIErQMT16+lpSik2hJyndefOmfK26tyR384cJiI8gvNnz3H54iV2hpyiRv3MUUQPHjzIhDGvMeqF/ixe9AOJiYk5aj5y5Aiehiu0rlkmfZhdoype1HZP5u+//zbrfYNp4XVRZm/5QUQmi8gxETkoIqtExC2LPHYisktEDojIERGZkOG1D0xl94vIRhEpa0pvaErbbyp3f4/P1Nz3NG7cmANJd/rhvsQzOfvhL2tp6eFNFU/j8FMLsaBLUA1CDx7Okx+eSIy6ww+3Rp3N1g9jrPzhaM0AACAASURBVFO5knAtPU0pxZbIY7Tt2iFT3jbd2rP5wn8mPzzP5YuX2HvpGDX/VzdTvoMHD/Lum2/wyrODWPzjj2b5oXtSNC2rlk/3w4aVylDDyaD9UPuhJp9kPZTfOGA9OTmZgIAAnnmmH599NoWTJ08ycuTI4pCpeYDRHVDNXePq6srzY4Yz9dwytobtYVvYHr48t4RBr7+Q49p5oWfOUt7xzrUIy9q7Eh4eblbbNjY2vPb+u0zYt5lfThzg99NHeG/3Jpr3eiJThMabiAhvT5zIh//tZsWRw/xx4gTv//MX5Zo0MUWmu0Xbju35/sA/zNvzJ8cjLrH8yB4++WsdHbrfmtO77pfVTH/nRTq7h/BKPUXcvwt46bl+Od7puHjxIv7ud9518He35GLYBbPe901UHv7yySagplKqNnACyOo2dRLQSilVB6gLtBeRh02vTVZK1VZK1QXWAuNN6YeBBqb09sA3IqKnBGjuWVxdXXl+9Ai+PLOcrRf2su3CXj4/syxXP7xw9iwVnO+MPlvO0TlPfvj6B+/y8eGNrDu1n81nD/PR/vW06N0tWz98Z8pHTD27mV/O7GH7+cN8HryeSq0b3OGH7Tq2Z8mJzfxwZCMnr1xgzcl/mLpvGZ163Bpuu271ar58bSQtk2N5zseNyHWrGD5wQK5+WNHV9o50fxcb7YfaDzX5xNramvbt22OVPvJLAWmAoJQFR44cZcGChRgMBsqVK5dDTRpN4fBAGdyJEyc4fPgwvr6+/O9//8vTYtvFQWxsLCuX/8yh3cEE1gygR+/ueV4UPS0tjd9//4NNv2zFzcOFXv26U6VKlQLT2OXxrjRq8jB/bPoDgyGNwW3H4OPjk2XeiIgIVi5ZzJFD/7H7VDiVPXywtzbOcTIoA6fjoqhUqVKWZW+SmJjIujVr2L9jBz7ly/PO1M9YuWw5cfHxvDZuEvXq1cu2bK1atfh+zRr++P13oqOieP2RR7Jc6P3HefMZ9VBX7KysORt9mZoVa9KuZnMWTJ9Ns2bNSElJYeHMz/nqSX9ioyNJiommU41SJOyLZuOGDXTu2jXTe96xYwf29vZUqlSJlaE36KdUpiuTu0KT6NfP/OUlCuhEyry2lNqY4ekO4Kks8iggzvTU2rQp02uxGbI6ZkjPuGi43c10TdFxL/rhqgx++FQ+/HDDL1so5eFK70L2w4Ft387VDw8f2MfecxcI9PLO5IcnY6PN8sO1a9aw/98dlClfgfFfT2HFsuVciI/n9YETc/XDH35dafLDq7z9yCtZ+uEPcxYwJLA3dpbWhMRepLpnbVo6tGL+13PT/XD+11P5+OG6xEZFcSMhjrblvEk4G5arHy6/fJ0+t/nhnvB4+tfUflhwqjUPKnPmzKFJk6ZcuRJJXFw8Sllw676TkJCQyNtvj+X555/P85rDGk1+eSA6oGlpabwx4i3O/XuJMokVuWb7G8llv2TWj9PzfAJTVERGRtL/iefxuVILH6sg9mwJZcWCAcxdNp0KFSrkXgHGyIjDnx9F6I5kylrUIzz1Os+tfIM3PxlCp84dC0xr6dKl6d23d455jh07xvjhL9LdvwxDK5VmXdRFXl47iw/a9kcpxbLT/9Gxb48cl2lISEhgaL9nqGtpSTufsmzfvJlnv/iUpxvXws/RjslvvsITg4fSo3efbOtwcnKi2+M5Ryf+799dvBPwGBZiQW3viunpkQf/AYzraLlxnfDQU5R2tsbKRrgWdZGKNkn8t2Nb+gnXooXfsWHJHNoGORCapJhzJhkfv0C+2HCSfk3KY2UpLN8VhsEzKM9LTeRxLpOniOzJ8Hy2Ump23qoAYDCwJKsXxBjXfS9QGZiulNqZ4bWPgP7ANaBlhvRGwLeAH/CMUioVTaGTlpbG6FfeIGLfeapYlGUzMXzllsTX388q0X448MnnKRdVC1/rIA5sDWXlwgHMXpo3P3zx+ZGc2JmIh2VtTqXGsWHVa7z98VA6F5MfPlmxLC9UKM26yIsMWTqfyV16YVCKRUcP0uHpXrn7Yd9nqI0NrUv78vembQyaMpnuDz2Ej70Dk0a9ypNDnqdnn/z54d5/dvOSVw8sxILqHreWXYwIMfbFQkNDcU5O5PK5s3ja2WJlYUFsRDi+qYns/fuvdD/88fv5/PrDtzzq605cqoFvwq9T1j+Ar7Ydps9DAVhaCj8fOIcqE6D9UPuhpgDw8fHhxInjbNiwgT59+nD9esJtOYSUlGQuXryIn59fsWjUPLg8EB3Q5UtWEP1nMl1d+4MpEv6JC4eYOP5TPp02sXjFZcOML77B/0ozApyMC3N74YvLdS8mv/8lX8/93Kw6tm/fTsjOROo43ppO4p0WwJQJ02nbrk2Rhtz+4oMJjG5UkwruxikzL7VvRekde3ln7y/UrlWH7uNeoVXr1jnWsXTRTzS2saVrteqkGQx8vmUDCx5vTJqVBRUD/Hm8bhqvfjeLRk2bmX1SmhXePj5cjovJFFHyRkoylnY2KKVIS0vj4Imz+Heoh6WF8cq9o60VW45Hc+pKKGAMf75l+RxmD6qGlaXxiuPjMQm8vuIsLQcN55M1y0lNS6FVx0EM7dEr78su5O0C+RWlVLaLe4nI70BWt2nGKqVWm/KMxbhw2I9Z1aGUSgPqmuZErRKRmkqpw6bXxgJjRWQMMAJ415S+E6ghItWA70VkvVIq54ljmnyzYulyOHCNIX63Oh4HIo8zecJEPpn6aTEqy54ZX3xDUFQzAp2Nfljazhe3OC8+e/9LpubBD0/siqey0607ch5p/kyaMI12xeCHbzWodcsPH2tF6Z17eXP7b9SqXYcnRr9mlh82tLSnS2At0gwGvti6nlkt25JmKVQMCKBzWhpj58zh4Wb588PSZbyJiI7Cx/FWZNrE1CQs7azT/fDwqTP41QzAynQX3dHGmr8uXOb0+RDA6Ie/L5rP1I4Pp/thp9h43vn7GC2fe4Epq1eQmppK6859GN5T+yHaD+9LlFKcP38eCwuLfO2TecHKyorOnTtTvXp1du7cza3ItwAKg8GAp6dnkWjRaDJSssdcFRBrFq+lvkPzTGlVnGqy758DGEfLlDx2bNtDJcfMV4HL2vtz7MBJs+vY9vs/lFY1MqVZW9rhkFKG06dPF4hOczAYDMRcuph+sgVgYWFB1wZ1qBxQgWnfz8v1ZAtg59YtPOJXEYBj4eFU83LCx9kRlZqKMiisLS3pUtmbP37bkC+9fYcO5oez/3IjxTh/KSUtjcWn/6Ves4Y883gnXh/Ym9Q0A6v3R6T/fi7HJrHmwBUiws4D8PuGdXSv55Z+sgXg7eZAkKcQGFSN6fMX883CFfTq3Rcrq7xdB1J53HKtT6k2SqmaWWw3T7YGAJ2BviqXHUYpFQNsxTiP6XYWAd2zKHMUiAfydttDc1f8uuwXWpXOfP5d2zOQQzv3lVg/3PXnHio7Zf55lHPImx9u+f0fXFX1TGlWlrbYpHoXuR9GZ+WH9evg71+Jr+d/Z54fbtlKs3LG+Z3HIi8T6OaGt6MThrQ0lDIYl1UpV57fN/yWL739hw1iZcQWElON0XFTDan8fGkL9Zv/j76duzKyTz9SDamsPxOS/vuJiL/B+jMhhIcY/XDT+nV0reyTyQ9LuzhS2cmawKrVmLHgJ2YvWkavPtoPtR/en+zbt4+goCCqV69OUJBx1FNwcHCRtT9+/HgcHDKO7lbY29sxaNAgHB0di0yHRnOTB+IOqCBkdegpoedaADi7OnEjPg5Hq1vBKVJUErb25o/T9/L24Eja5TvSk4jFze2O4H2FhoiQJhakphkynYBEXo+nlLuH2fWU8vAgMi6OUhkWdL+5CPnNK+Yqw+O7pXGTJlx980UmTp2BY5olsYYkHunYhr1/rOOjtrWIiPVm3IpznIy4wYD5wTjbWZFqULSo6sGOG27p7zmrn1dB6AMw5DOao7mISHvgLaDFbfOUMubxAlKUUjEiYg+0ASaZXquilLrZS+gKHDOlVwJClVKpIuIHBAHnCvXNaICbv817zA9dnEi4HIeTdQY/NCRh42C+H5b29iDJEHpHegrXS4wfunmYPwTazcODKwlxlHLIePKoUBnnVKqC8MPGRI17jhlfzMIuxZp4dYMWXVuz67f1jG3QhMgKcXwYEcaZmFiGb9qOk7U1qUCzCr4cMAWbE5Esf1/aD7UfPgjExMTQsmVLrl27zs07kMHBx2jevDmhoXd6UmHQsWNHZsyYweuvv058fDwAgwcP5IsvviiS9jWa23kg7oB269uFPQl/Zko7HneQBs3r5engl5iYyNxZ8+je7mn6dRvIL6t/yfMdA6UUG3/byOCnnqVHu57M+HJGuhlkZMCLvdmdtBaDMq6HZlAG9txYT6/BT5rd1pM9unHJfic3Um8tPXIh/jDlq7tTpkyZHEoWLCJCy85dWLw/OP3zSk5NY96+ozw1cLDZ9fQYOJCFx46QmJJCVW9vgiPiOB0VjZOLC4ixzrWnwmnTvkPuleVCp65dWLZpHdPWLGLFlg3Y2VrydPUyeDg5UK2sJ7GJUNvXmem9Anmvsz8fPx7I2kNXeXb4qwC0ad+J5f/FkJJ6a3bSxavxnIiCqlWr5ltfEa57Nw1wBjaZlgiYBSAiZUXkV1OeMsAWETkI7AY2KaXWml6bKCKHTa+1A14xpTcDDojIfmAVMEwpdSX/cjW50alXV/4I350pbd+VY9RtUj/Pfjhv1jye7tCTQU8OYO1d+uGm3zbyXK9BPN3hKWZOzdoPn3mxN/8kZvbDfxLW0zMPfti9Rzdi7faSlMEPw+ODqVTNo8j9sFXnLiw+eJsfHgimRx78sOegAfxw8hCJqSlU9fLheEwMZ6Kv4uTqAgjJqan8FnaBth2yuvmWNzp368zKzWuYvf57ft6+DlsbC54o74+7gyNBXt7EpRio4enOxBaNePPheoxrUp/fzl3guZeNu3vbDp1YfeoSKRnW97x07Tqn41O1HxrRfngfs3jxYlJSUjGecgvGSLRCUlIyq1atKjIdAwYM4PLly5w+fZqoqCimTZumgw9pio0H4g5o9x5PsvuvPazePp8yyZWIsY7E4JfArAnTza7DYDAwpN8wbI/40NyxD0mGRBaO+YXgA8d5a/zrZtcze9psts39m5bObXGwcmT/93t5dtNzLFj1faY5SB07d+RSWASL50zH2eBJLFfoPKgdfftnH1Didry9vZn8zbuMf/1juO5IMgkENizHp1MnmV1HQfHCSy/z+SfXefm3rfg42XMh7gZPDxlK48aNza6jXr16PPHSS7z59df42tmT6OjCa38e58mHgnAMi2FbaDQ9XhhRYCHFLSwscHEx3nGJuHiRRq637jS83+kRxvy8GU9nK8q42nIgLB5x9KR5c+NQ70qVKvFY72EMmT+DVoEOxCYq/jmfyruTZ+Y72qhxKFmRRX2snE36RaCj6fFBIMtwm0qpO4aYmdIXAgsLSKYmDzz5VHf2/rOHWbtXUdmyLOEqhuseaXz97kyz6zAYDAzrP5QyoW709ehC4o0k1ny4nGOHjvL6uDfNrmfO9NnsWLiFTl4tcLJ3ZNfyAzz/x2DmL194hx9eDotgyZzpuChPrnGFTgPz7odfzHqPsa9/RGqcA6kqgaoNyvNZMcx7feGll/n8ehwvb9qCj5MDF+Ju0PuFvPth91eG8/ZX0yhj60CysxPjDu6jS40aOFy5wj+REfQa9mIh+eElajo7p7825pF2TNi8Fg97W7wdHTgSdRVLV49Mfthx4BBe+W42zX1LcT3FwK7IOCZ8OU37IdoP73fCwsJISEgALDOlJyUlExYWRtmyZYtMi6WlZZFecNNoskNK6pyf/NKgQQO1Z8+eTGmnT58mODgYX19f6tXL293P7du3M3PYIpo73Aroo5RidfIsfvh9jlmTuOPi4uj5aC8GugzFQowHXYVi3cXVODa3o33nx2jXri22trfWRktKSiI8PBwvLy/s7e3N1psRg8HAxYsXcXBwKJAolwaDgb/++otjh4KpWNmflq1amn0VLT4+nqioKMqUKXPXV95SU1O5ePEipUqVwsbGhr///pvExEQefvjhQovi+f1333Lu5+/oX78yTs7OXDh/DjdbG/ou+ZeeDR6idVBltp4NpezjPenes2d6uatXr7Jjxw4cHBxo0qRJjoFORGRvTsExbuJiWVE1cHrXbO1bYgebVa/m/qUw/PCnN77jSZ9bow2UUsy4vJA5674z2w/7tOnByHLPpPshSrHszHrkfy6079yetveUHx4x+WGr+94P53/7Hcd/WEyvqjVxcnLmwvnzuNrYMHTLerrX/R8tKwex/cI5KvR8nKe0H2pKGEFBQer48eNF1t66det4+unexMUlcCsIkMLR0Z71638lLS2NRx99tMj05MbWrVu1nhzQenLGXO9+IO6A3iQgIICAgIC7Kntgz0G8UzOvySYieBv8OHnypFknXOfOncNHyt7qfCoD586G4HTdnR0rjhCyfTNffzqH+Utmpl8Rs7W1zXe0NAsLiwK7Cp6QkMCw/s/jfsWCKnY+bEnexbwvZzLjh7l4eOQ+n9PR0THfE96trKwyfSatWrXKV325sW7NGtbO/57YyxexTrxB3bKuXLuRzPqTkYxq3YJHA40Xxev5eLH5v72ZOqDu7u507FhwSzzAzXmv9+eFI03RkR8/PLT3IP6W5TOliQh+lr558sPy1t4Z/FARcjYE92hXtq08S/T2rUz/dC7zlswo0X44vP9zeMZAoH1ptif9y7dTZzJ94bz72A9/Yc2cH4i5dBHLuGRqenpwPSmRPy6F8lLzNjQPMK4lWtvTm3/2/gfaDzUPOO3bt6d69WocPHiIxMRkbgYAatjwfzRr1oxt27YVt0SNpsh5IOaAFgR+lf2Itgi/Iz3a4jK+vr5m1eHj40NUhmkdV69Gk3bDkhhDPGVdalDVqQ1lo9vxzpsfFpjurIiKiuLUqVOkpKTkuez82fOoFlOKPpVa8r8y1eju14zWEsSXH08pBKXFg8Fg4MyZM1y+fJnY2Fjmf/45k5q3YFavfni5BbAy+Bofbg5mcNNH0jufAGeuXsO3kn+WdV26dKlANao8/Gk0BU2FAD8uG+6cohZuuJInPwxPuZr+PDrqKoYES2JSE/FzqsVDDu2oHtWe9978qMB0Z0V+/bBmnAvPVG5OI9+q9PBvwmPWlfnyk8mFoLR4uN0P5346lXfqdOHzDi/g6e7P2tDLfLZ/FwObNE/vfAKcvxaNb6VKWdal/VBzL3Ds2DGWL1/OwYMH81WPpaUlW7ZsYdy4sQQGBlC1aiATJrzHhg0bCiQIl0ZzL/JA3QHND+3ateWbKfM4H3USP8cqGJSBw3E7KN+otNlX5D09PanerBp/bdtKY7dHiImOJSrtOgfSDtCylDEWgZdDJXYc+RWDwZDvuTG3k5CQwNhRYzm/7xyuFi5EyBWGvT2MTl06mV3H1l9/Z1TZzPnrla7Cup1LC1RrcbFzxw4mj52At4UD8SlJRFok0dzdHTvT8LhONWrSqUZNRq1awbZTZwn0KY21pSWnI6NYGxbOrB490uvavWsXU94bSwV7S2KTUpBS3nzw+VS8vLyya95sCiCYhkZz17Rt15Z5X8zhRMwZAt38MSgD/17ZS+maZfLkh0FNavD7rr9p6f0w12JiiUxOYGfcUbpVfA2Asg7+rDu8ttD88O2R4ziz9xzO4sZVywhGjHsxT364bf3vvFmhbaa0h3wCWL2z6AKLFCY7d+xk4pgP8ExzIiEtkavWCTSy98HWyuiH7So/RLvKDzHuj+/56+xpqpT2wdrSkjNRkWwID+Obnrf54fhx+NpaE5ecgnh48eEXX2o/1JQ4kpKSePLJJ9myZSvW1takpqbQoEED1q1bh5OT013V6eDgwNixYxk7dmwBq9Vo7k10B9RMbG1tmbtkJh+Pm8Tyvb8iltCyxyO8OiZvASwmfPoe0z6bxvyfZ3EuPhQHVY4mFZ/F1tK4tIhSCoWhUK6KvffWe7jtd6G1x0AAktKSmPPubAKqBJgdidDaxpqktBSsLW/9dNJUGmJ5799Mj4yMZOLr43ijajvc7IzD4pYH/835C6ehbuaYEo0CqnDO04NXNv+LGAx4+1Xik1mz0+dcXblyhSlvv86kdnUo5Wicq7Y/NJwxLw9j7k/L8qVTSdEtO6DRZIWtrS0zF33Dp+Mnsm7/ZrAQmrdvwaTR7+epnncnTmD659P4Ys1CQi6G4JrmR7vyQ7GzNO5/SimUpBWKH7775gTsdpXiaVdjBzI5LYmZ4+bmyQ+trK3u8MNUg+G+8cOPRo5nWLluuNgaT7rXnNpGSMRp4yIhGahfoSrhlRx4c8cWxGDAp6IfE+fMyuSHk0e/yYeP/I9SDkY/PBh2mdEvDWfe4vxdvNR+qCloxo0bx+bNW0lMTOLGDeOQ2Z07d/Hyyy/z7bffFrc8jea+QHdAsyE0NJSpE6cRvO8ozq7ODBjel46dO/LV3PytmWRjY8OrY17l1TGvsn79Bj5/bSnONl7Exl4nIvwKYUkHUAHxnD17Fn9//9wrNJP4+HiO7ghmiMdz6Wm2lrY0t23G0vlLGD/RvCAOXXt3Z93s9fSs1CL9pPCPsH207NQ2l5Iln3Wr19C6VOX0zidA16BGDF6zncOnT+FgMJ7kGGxs2B4ZwcL532V7NXT92l/oWtkrvfMJULe8N6uO7efMmTP5/m716ZamKAkNDWXap19x7IDRD/u+8AwdOnfk89lf5qteGxsbRo1+lVGjX2XD+g3MenUZbtZexF43+uGZpH3EVy4cPzzyTzB9XV68pcXSlkYWLVjy3VLenTTerHq69n6KNd+upXflZul++HvoAVp2aldgWouLtT//QlP76umdT4AOlZry0ubNBJ85gV2qaa1jO2t2Xr/AoilrsvXDX3/5hY7ly6R3PgFq+/rw85lQ7YeaEse8efNITEziVsAgISkphUWLFjFv3jw9bFajKQB0BzQLIiMjGdJjGI1utKOnY0vio67z7Vs/EX0lhr4DzQ/7nxvt2z/GoX3B/DRnCsmX3EiyuIGFgwV+qj39eo5g6c+zCzRYhoOFwx3G6WLjQuiVMLPr6dG7FyeCj/PZ1lX423lzITmK0jX9eGPkiALRWZxci4rG3dohU5qNpRViYc17W7bQzLccliL8fTEMj6pVcwweEht9lUr2d0Z49LC34dq1a/nWqoNuaIqKyMhIhvd+gY62zXjMqx+xyXEsef97oq/G0CcPy6DkxmPtH+PwvmCWzpmEXCxFnGU8KY5CGdWWPj2Hs/znOQXqh/YWjnf4obO1C2evnDe7nh69e3Hy6DEmbfuFyg5ehCRG41mjIh+/cn/4oYtVZo+ztrTCwsKKT/9bSyPPSlhgwa6os5SuVSVXP6xgl4Uf2tlqP9SUOG7cuJFlenJyCmlpaVhZ6VNnjSa/3PvjhAqQ8PBwvvl6FkN6D8H5kjd+jpUREZysXejg0osFMxaRmppaYO2JCG+MGYWDlw3elWpRPbALDSoPxsspEMcbjflmxvwCa8vT05MUpxSuJWc+2B+IO0iLDo+aXY+FhQXvfPQeX66YS+cPnuXDH79iysypOYbTv1do2vpRdsacy5R2/MoFfOyt+WngIFrXqcMjtWqxoP8AAq1t+O+//7Ktq3GLlmw5ZwzSogyKmOgYzp0PYevxc2YHacmOvATc0EE3NHdLeHg4s6fPYmi/5ygTW4oqbpUQEVxtnelTris/zVpY4H742phRWJW2xsq/Kr5BHahRZQDuzlWwSWxU4H6Y5pxM7G1+GJy4n5YdW5hdj4WFBeM+nMDny76l/bsv8P7CaUyZ8dV94YfN2rRgX8KpTGmnokMobWPHnO4jeLTmQzStUZtZTw7DXznk6IdNHm3JtjBjED+DyQ/Png/hz5OntR9qShytWrXCwuLWcinGWcYGGjVqqDufGk0BoTugJnbv2s3gzoMI/zGMqmcCMMRfY8mZbzCoNACsLKxxTHMiNja2QNtNSUnBkGKDj2ttnOy809PdHCpy5FDBrVMlIrw9cSw/xS7hv6h9nL1+jl8j13MjMJGOXfIeFt/Hx4cmTZrke0mEkkT9+vVxq1eF2cFbCI4MZVfYCb449BuPVKmCtZUVtcqWpa6vLzZWVlRzdubUyZPZ1lWvXj0cgury8ab/WL9jL9sPHuODDbup4+nGiGd6ExISki+tBpTZm0aTV3bv2s0Lj/cnftVJGkaUISX2GjP3f0+aMoZ7sbawwkUcC8UP01KsKe1WC8cMfuji4MehAvbDcZ++zerkHzgYvZeQuLP8EbOWtOoJ2g9N1K9fH69GFVl4bgPHr57jv4hjfHP6Zx6uVBUbSyuqly5PLW8/bCytqGzrnqsfOtWozaQ/d/Lbrj38dSSYT7b8Qy13d4b37aP9UFOimDp1Kq6uzogYgDSMnVCFjY0NiYmJxaxOo7k/0B1QjIEuPh79Ec+UepoGng8R6F6Zds4t8TA4EhyzD4BUQyrxlnG4uLgUaNvW1tbYOkBqWlKm9JgbIVSvFZhNqbujwf8aMGfNXDx6exHVOJpuHz7BrIXf5OlqfWJiIrt37+bo0aMolfeDeUhICDt37iQ6OtrsMkopjh8/zq5du0hISMhzm+YiIrw/+RN6fDCKE1XsuNa4PO9+NZkwdWeMxePxcfjnsIaiiPDeJ5+SVrEGv566QliKDW93bce4Ds15pY4/Uz+++6V2FJCKwexNo8kLSikmvf0Bwyp0pUnZ2lT38udx7yZ4KXv+Cz8EGP0wVsUXih/a2d/ph9dvhFKzEPzw27VzKD+oFDdaRtBzUle++WGW9kMTIsIHUz6m36fDCXtIkdrGg/dnTCLC6s7las4kx+TuhxMnQZVq/BZymYsGK0Z37MCYNq0YXi2QLz+6+6V2tB9qCprKlSvzxBNPYGlpCdzadu3azbhx44pZnUZzf6DHEgCXLl3CPsEOkGEQTQAAHBVJREFUF09nADw83TkbfZ7qNoHsijlEJeeqbI3/hT4jexb48AsRYfjIQUx5bzHl7Dpga+3MtRsXiLP5iyFDvynQtgDKlCnD8FHD76rsr2t/5ev3p1PO4EeiJJDsmcgX8z5PXyQ+J4xLHrzGtZOhlLd347O4CJp368jwV1/JcUJ/eHg4bwwdgUtcKq7WdkyMu8LAkcPo+uQTd/UecsPCwoKmTZvStGnT9LQ1ixezKjiYToGBWIjwx+nTRDo40KBBgxzrEhEunTnFlKfaY5vhd1OtjBche/7Kl059JV9TWFy6dAmnZBtcTcFn3D3cORdzjrrOAfwdEUw198r8HL6JHkOeLhQ/HDFyMJPeW4S3/WPYWDtz/UYYidb/MGTo7AJtC/LvhzM//ooAq7IkqETiXFOZMvtLs/1w3KuvEnfmHH5OzkyNuUrTzp0YNmqUWX7oHJeKq5U9E+Mji9wPf160lHVn9tHWrxYWIvwZepToUtbm+eHpU3zctXMmP6zq7c3Xm7bkS6f2Q01BopRi8eLFpKYayBiIKDExmTlz5jBlyv2z7rlGU1zoDijg6OhIguHWlWQrK2v8Airwz+kdnEk+ymbXZfQf3Ycuj3cplPZ79HgSZydHZnw1n4irsQTWrcSX474qUcO5QkJCmDF+Fv1cnsXW0haA0OgQXhvyBj+t/THX8l9OnExghIFWtY3RIQ3KwKx129hUoxrt2j+Wbbm3Xx7FU47+BFU0ntQlp6by8RczqVGnNgE5XHEvSD6dNo15M2fy1vr1KKVo3Lo1U0eMMCsSnrOLKzEJiXi73IoOmZKWBpZ3v+spwKCD8GkKCUdHR+JTM/ihtTUVKlXgz+N7OJx8muSU9fQZ8wxduhWeHzo5OTL9q++IuBpLUB1/xoz7usT54dwPpjGyQi9srYx3TM9eC+PNF1/lh9WLcy3/1aRJ1LqeQLsmjwBGP5y66Q821ahBu8dy8sNX6e7gT1AF47zJ5NRUPiliP5wy4yvmzpjFh+t+w2Aw0KxtK75+ebjZfnjtxg1KOzunp6WkpSGWlnetR/uhpjAwDrW984dVmKMONJoHCT0EF3B1dcWvTkUORR9JT1OWihNup1nxxzKWb1xM1ye6mnWA3bZtO50f60Xj+h3p0OYpftuw0SwN7Ts8xpr1P7F95zrmfT+NKlWq3PX7KSiUUixbsoKOzZ+kQ5Nu+ISVJy0pLf318o4VUOGKc+fO5VrPzs3beLRCtfQ0C7Gge6V6/Pxj9idrFy9eREXGEuR1646CjZUVHX2C+GX5yjy/n7S0NL75egZPPNqBbk3b8dKgoblqB+Oah8NGjmTJb7+xdONGRr31Vo4RHzPyZL/+fLv3KAbDrSv0Sw+coGXn/Jy8mz/fSd8Z0OQVV1dXytUMYG/EsfQ0g6VwwO4CizeuYPH6ZXR9PG9+2CSPftihw2OsXb+Yv3f+yrclyA+XL1lB1xZP0uWRblSOLk1a8q0gTJVcfbGMMpjnh1u30abyrfdkIRY8Xa0mqxf9lG25ixcvYoi8RpDnraA9NlZWdPCuetd+OHPqTLo060zHhh0ZNmCY2X44fNQrLPv9V1Zs3sCoMW+a74fPPMP8A4cxGG4NhV1x5CitunTOs/5baD/UFCwiQuPGjbl9gR8RaNHC/CBlGo0me/QdUBMffPYho196iz3H9uFq4cIlwhn27nACA82fd7Rz5y7eGD6ZcrbdqGjtQnJMHO+8NgtLK0vatGldiOoLh8WLlrLgw/XUs+sPaj0Oqe6EngvDL6A8drZ2ANhgm+ukfKUUFggWkvl6h4O1LTdisg53DsYrkA5Wd87HcrC25UJ83q9Cfvr+J8RvP8Nbfo9jbWnF6egwRg14kW9/XkSpUqXyXJ85tH3sMULPnWXYssUEujtz/lo8QQ0bM3r4S/mqV59IaQqTCVM+4u2X3+SvUyspZeVEaOoVXhj78l35YXnbx9P9cPw97IdLFy1l+Sfr6ejYn7/kV1wNjlw4F0YF//LY2Rn90E6szfRD7vBDRxsbEnO4u5KYmIiD5Z1+6GhtS9hd+OGkCRMJX3+JZz0GYG1hzfkzIbzUdwQL1i4sdD98ZclSKru5EHo9jqqNHmb0CO2HmpLFjBkzaNasGUlJKSQnJ2Nra4OtrQ1ff/11cUvTaO4LdAfUhIuLCzO+n8nly5eJiYnB398/z6H0v5zyDT7WHbC1NgbmsLFyopxdJ776bO49d8KllOK76YtoaP8s1ha2lHetxeHrm6hg1Y0rEVGUK+9LXEocV22uEBgYSFJSEr+sXs2urX/iXtqLp/r1pXLlyoBxHlH5oMocvxKW6er95tBgWj7VPlsNFStW5LK6QfSNOErZO6Xr2hp+ir4vj87T+4mNjWXP738xJvCp9Ds3AaV8aRofwM/LVzLo+Wfz+hFlIiEhgdUrV3Bg5z+ULluO7n364efnh4jw7NAXefqZ/oSEhODt7Y27u3u+2lJAmj7h0hQiLi4uTJs/K59+OJsyd/hh53vWDxfMWEQ3x2exsbSlskttdkeso7d9W6MfVvDlenI8ly2i0/1w7erV7Nq2DXcvL7r365fZD6tUITj8MtW9fdLb2Hj6BC06dshWQ8WKFQknCz+MOEnfV/Luh/+u/5cXPJ9N90M/5wrUuVKLVctWMXjI4Lx+RJlISEjg55UrOPDvv5T29eWpvtoPNfcWtWvXJjg4mGnTprFv3z7q16/PiBEjzJrjrdFockd3QG/Dx8cHHx+f3DNmwaWLEZSx9cyUZmvtQuTV6wUhrUgxGAykJCisrYzzPcvYV+ac0z7WxC0nSPkT5niew5b7eG/au6SkpPBi/wFUTYKuZf2IOBPO+Ode4IV3x9GiZUsA3np/PK8MfI76MZcpZ+/KodhwYrxsGdmnd7YaLCwsGPPJB3w46i0eLeWHm409/14NwffhujRq1ChP7yciIgIfW7c7hg36OXkTfPJsHj+dzMTHxzNsQB+ausEzFb0JvXKIt59/hpEfTuF/DRsCxnl11apVy6Um89FX/DVFQf78MJyytl6Z0mytXYi4R/0wLUFhY2/0Qz/HyhyzLcPC8PXUSPLllNVFdiUdZeyX75OSksKwAQOokZZK9wrlibgYxntDhvD8O++k++EbE95j1OBneTgyAj9nZ/ZFRRHp6sRXffpkq+GWH46mhZsfbtb2/BsdQrnGde7KDz0tPe7wQ1/bspw7nn8/fLFfPxrZWfOUry8Xzp7irUEDeW3iJO2HmnuKcuXKMXHixOKWodHcl+gOaAESVNWf0N2huDqUT0+LT4rEN6B0Maq6OywtLXEsZcON6OvYWzkjIjQp25MDUb8TGXiaFn0eZkyX13B3d2fl8uUEJUKvGvUAKO/mTqCXNxM+nsgjLVpgYWGBr68vC9esZOOG37gYEkqXh3rQtGlTLCxynoZcv0EDvl2znA2//kpMVDTDWw6lZs2aZs0/y4ivry8Xkq6SZjBgmaHNo7Gh1GjQNu8fUAaWL/6JR0sJTz1knNNVwcOVQG933vvkfb5f+UueteaGcVlsfcKlKdkEVQ3gwn3kh3albIiPu46jyQ/b+/bir4hNnPQ/Q/ferRnWZXy6H1ZPS6VPndoAVChViqDSXoyd+EkmP/z+51Vs3LCBS6GhPFavXh78cFm6H45o+cJd+2G4IZI0lYal3AoAdDrpDA83bJpDydxZtvgnmtrb0q2GsYNZvpQbVTw9mfTRhyz4ebX2Q41Go9HcO0GIRKS9iBwXkVMikrfxRkXEa28N56rlJqLjz6KU4lrCBcLT1jLmnVeKW9pd8eq44exKWcTVpIsYlIGQuGCueR1j5vzp9BvQL33o1K6tf9K4bOYIlc62dnhYWBMeHp6eZm9vT7cnHufFV17ikUceyfVk6yalSpWid9++vPjyCGrVqnVXJzD29vY8PqAXc0+u50rCNVINqfwVdpAj1pF07Nwpz/VlZM9fW2lRJfOwHC9nRyyT4omPj89X3VmjSBOD2Vt+EJEPROSgiOwXkY0ikuX4o+z2TxFxF5FNInLS9L9UhtfGmPIfF5HsQ39q7uDe8MNhRFlu5Gq6H4Zy+R72w5fHDufXG4uISDT64cnrwYR5HGf6d9PpO+CZdD/cvW0bTcuXy1TW2c4OT0urLPzwCYa+/HKx+OFTg59i6eUVRCdFk2pIZXfkXs65hdCxS8c815eR3dv+pKlf+Uxpnk5OWCQkaD/UfligmPt9aDSaksc90QEVEUtgOtABqA70FpHqxavqTgIDA/lx+TSqPBLJFftFVGh4nvlLPqNOnTrFLe2uaNW6JZMXjCWhzl52O83Bp3MMC1bNokyZMpnyeZbxITwuNlOaUoropBsFvlB9fhg05Fl6vvsiy9R+Pr+0HtW6ArMXz8fBwSFf9XqU9uFSTOYTK4NBEZ+clh6cpCBRQAoGs7d8MlkpVVspVRdYC4y/PUMu++do4A+lVBXgD9NzTK8/DdQA2gMzTPVocuHe88MIrtgvonzDEOYvmXJP++H788dypsYeVtvOweKxGOauvNMPPcr4cDk28zBjpRRXExNLlB8OHjqYAZMG8YfbNhakLsKleynmLfs2337o6ePN5euZ37/BYCAuNVX7ofbDgibX70Oj0ZRM7pUhuA2BU0qpMwAishjoBgQXq6osCAgI4OsZk4tbRoFRv3595vxQP8c83fv0ZvTAZwny9MbV3gGlFBtOH6Vao4Zmh+cvKtq0a0ubdvkbcns7PfoP4rPXhvGRlxtOdjYopfhp70kat2mPlVXB72K2dlZUqeZhdv5L/9x9W0qpjFcWHLk9Lr2RnPbPbsCjpnzfA1uBt0zpi5VSScBZETllquffu1f7wKD9sJioX78+9Rfm4oe9+/D24MFU9S6Nm709SinWHT9BtYYlzw/btmtL2wL2w54DBvLpyy8x3t0dR1tb4/I1R4Jp2q6d9kPthwWKmd+HRqMpgYhSJX9/FZGngPZKqedMz58BGimlRtyWbwgwxPS0JnC4SIVmjSdwpbhFmCgpWkqKDihZWoKUUs65ZRKRDRh1m4sdkHFtiNlKqdnmFhaRj4D+wDWgpVIq8rbXs90/RSRGKeWWIW+0UqqUiEwDdiilfjClzwPWK6WW5+F9PZBoPywwSoqWkqIDSpYW7YfaD3Mlt+8jQ76S6Ic3KUn7HWg9uaH15IxZ3n2v3AHNapLLHT1n00FkNoCI7FFKNShsYblRUnRAydFSUnRAydNiTj6lVPZr19xdu78DWYU6HauUWq2UGguMFZExwAjg3duryEpmbs3eRRmNEe2HBUBJ0VJSdEDJ02JOPu2H9zcF8H0AJdMPb6L15IzWkzMlUY85+e6VDugFIGNUg3LAxWLSotHcVyil2piZdRGwjjsP8Dntn+EiUkYpdUlEygARZpTR5Iz+7DSaQkL7YcmiAL4PjUZTArknghABu4EqIlJJRGwwTtZfU8yaNJr7HhGpkuFpV+BYFtly2j/XAANMjwcAqzOkPy0itiJSCagC7Cpo/fcp2g81mmJA+2HJwszvQ6PRlEDuiTugSqlUERkB/AZYAt8qpY7kUszsOR2FTEnRASVHS0nRAVpLbkwUkSDAAJwHhgKYwt3PVUp1zGX/nAgsFZFngRCgB4BS6oiILMUYmCMVGK6USivKN3avov2wwCgpWkqKDtBackP7Yckiy+/DDErab0vryRmtJ2fuST33RBAijUaj0Wg0Go1Go9Hc+9wrQ3A1Go1Go9FoNBqNRnOPozugGo1Go9FoNBqNRqMpEu67DqiItBeR4yJySkRGF3Jb5UVki4gcFZEjIvKKKd1dRDaJyEnT/1IZyowxaTsuIo8VgiZLEdknImuLU4uIuInIchE5Zvp8GheHFhEZZfpuDovITyJiV1Q6RORbEYkQkcMZ0vLctojUF5FDpte+EpGsQvZrNHeg/VD74W06tB9qHnhE5AMROSgi+0Vkoxjn8BannskmbzgoIqtExC33UoWqp4fJJwwiUmzLexTl8ctMPXd4WDFqyfJ4W4x67ERkl4gcMOmZkGshpdR9s2Gc8H8a8AdsgANA9UJsrwzwkOmxM3ACqA58Cow2pY8GJpkeVzdpsgUqmbRaFrCmVzGGI19rel4sWoDvgedMj20At6LWAvgCZwF70/OlwMCi0gE0Bx4CDmdIy3PbGKMhNsa4Vtx6oENh/ab1dv9s2g+1H96mQfuh3vSmFIBLhscvA7OKWU87wMr0eNLN/aAY9VQDgoCtQINi0lCkxy8zNd3hYcWoJcvjbTHqEcDJ9Nga2Ak8nFOZ++0OaEPglFLqjFIqGVgMdCusxpRSl5RS/5keXweOYjzId8N4woHp/+Omx92AxUqpJKXUWeCUSXOBICLlgE7A3AzJRa5FRFww7qjzAJRSyUqpmOLQgjHSs72IWAEOGNdWKxIdSqk/gau3JeepbTGuFeeilPpXGffsBRnKaDQ5of1Q++HtaD/UPPAopWIzPHUEijUap1Jqo1Iq1fR0B8Z1YItTz1Gl1PHi1EARH7/MIRsPKxZyON4Wlx6llIozPbU2bTnuV/dbB9QXCM3w/AJF9IWISEWgHsZev7dS6hIYfyRA6SLS9yXwJsaQ5DcpDi3+QCTwnWn421wRcSxqLUqpMGAKxnD3l4BrSqmNRa3jNvLatq/pcWFq0tyfaD/UfpiO9kON5hYi8pGIhAJ9gfHFrScDgzHe2X/QKbbj173Gbcfb4tRhKSL7gQhgk1IqRz33Wwc0q7kghX5lS0ScgBXAyNuurN2RNYu0AtEnIp2BCKXUXnOLFJYWjFfZHwJmKqXqAfEYh1cVqRbTfKJuGIdwlQUcRaRfUeswk+zaLk5Nmnsb7YfaD29Vqv1Q8wAhIr+b5jrfvnUDUEqNVUqVB34ERhS3HlOesRjXgf2xJOgpZvS+bgZ5ON4WOkqpNKVUXYx38BuKSM2c8lsVjawi4wJQPsPzchiHGBUaImKN8cv/USm10pQcLiJllFKXTEOGIopAX1Ogq4h0BOwAFxH5oZi0XAAuZLj6sRzjCVdRa2kDnFVKRQKIyEqgSTHoyEhe275A5uE4hf6b1tw3aD/UfpgR7YeaBwalVBszsy4C1gHvFqKcXPWIyACgM9DaNLy8UMnD51NcFPnx614jm+NtsaOUihGRrUB7INuATffbHdDdQBURqSQiNsDTwJrCaswUfW8ecFQp9XmGl9YAA0yPBwCrM6Q/LSK2IlIJqIIxoEK+UUqNUUqVU0pVxPi+Nyul+hWTlstAqIgEmZJaA8HFoCUEeFhEHEzfVWuM4+SL/DPJQJ7aNg1Luy4iD5veQ/8MZTSanNB+qP0wI9oPNRpARKpkeNoVOFZcWsAY7RV4C+iqlEooTi0liCI9ft1r5HC8LS49XmKK3iwi9hgveOa8X6lijuRU0BvQEWM0qNPA2EJuqxnGIQEHgf2mrSPgAfwBnDT9d89QZqxJ23EKKXof8Ci3oj4WixagLrDH9Nn8DJQqDi3ABNNOcBhYiDGqYpHoAH7CONcqBePVvGfvpm2ggUn/aWAaIIX5u9bb/bNpP9R+eJsO7Yd6e+A3jHeNDpv2x18A32LWcwrjfMebvlncUXmfMO2jSUA48Fsx6Siy45eZeu7wsGLUkuXxthj11Ab2mfQcBsbnVkZMBTUajUaj0Wg0Go1GoylU7rchuBqNRqPRaDQajUajKaHoDqhGo9FoNBqNRqPRaIoE3QHVaDQajUaj0Wg0Gk2RoDugGo1Go9FoNBqNRqMpEnQHVKPRaDQajUaj0Wg0RYLugD6giEh5ETkrIu6m56VMz/2yyGsvIttExDIP9Y8QkUEFqVmj0WgKA+2HGo3mQUdElIh8luH56yLyXjFKQkQGikikiOwXkWAReb6Q2/vH9L+iiPQpzLYedHQH9AFFKRUKzAQmmpImArOVUuezyD4YWKmUSstDE98CL+dPpUaj0RQ+2g81Go2GJOBJEfEs7IbycgEPWKKUqotxTeePRcTbzDas8qpLKdXE9LAioDughYjugD7YfAE8LCIjMS5q+1k2+foCqwFE5FHT1f+lInJCRCaKSF8R2SUih0QkAEAplQCcE5GGRfFGNBqNJp9oP9RoNA8yqcBsYNTtL4hIDxE5LCIHRORPU5qliEwxed1BEXnJlN5aRPaZ0r8VEVtT+jkRGS8ifwE9RCRARDaIyF4R2S4iVXMSp5SKAE4DfiLiLiI/m9rdISK1TW28JyKzRWQjsEBE7ETkO5OWfSLS0pSvhsmn95vqqGJKjzM1NxF4xPT6KJO+uhk+j79vtqm5O/J8dUBz/6CUShGRN4ANQDulVPLteUTEBvBXSp3LkFwHqAZcBc4Ac5VSDUXkFeAlYKQp3x7gEWBX4b0LjUajyT/aDzUajYbpwEER+fS29PHAY0qpMBFxM6UNASoB9ZRSqaZOoR0wH2itlDohIguAF4EvTWUSlVLNAETkD2CoUuqkiDQCZgCtshMmIv6AP3AKmADsU0o9LiKtgAXAzQ5ifaCZUuqGiLwGoJSqZergbhSRQGAoMFUp9aPJ12+/IzsaeF0p1dnU9lVgIDDSVN5WKXUw109Tky36DqimA3AJqJnN655AzG1pu5VSl5RSSRivRm00pR/COGzhJhFA2YKTqtFoNIWK9kONRvPAopSKxdiZu33KwN/AfNMczJudtTbALKVUqqnsVSAIOKuUOmHK8z3QPEM9SwBExAloAiwTkf3AN0CZbGT1MuX5CXjB1E4zYKGp3c2Ah4i4mvKvUUrdMD3OmO8YcB4IBP4F3haRtwC/DPmzYxnQWUSsMU7DmJ9Lfk0u6A7oA4xpOEFb4GFglIhktfPfAOxuS0vK8NiQ4bmBzHfV7UzlNRqNpkSj/VCj0WgA493KZwHHmwlKqaHAOKA8sF9EPAAB1G1lJZe6403/LYAYpVTdDFu1bMosMb3eSCm1Kod2bmqJz5CWpR6l1CKgK0ZP/s10FzVbTNMoNgHdgJ7Aopzya3JHd0AfUEREMAbdGKmUCgEmA1Nuz6eUigYsTcMq8kogcDhfQjUajaaQ0X6o0Wg0Rkx3GJdi7IQCICIBSqmdSqnxwBWMHdGNwNCbwX7EGEX8GFBRRCqbij4DbMuijVjgrIj0MJUVEamTB5l/YpyPj4g8Clwx1ZlTvkCgAnDcNJz3jFLqK2ANcPt8zuuA821pc4GvMI56uZoHrZos0B3QB5fngRCl1CbT8xlAVRFpkUXejRiHMeSVpsDvd6lPo9FoigrthxqNRnOLzzBOObjJZFMgn8MYO3UHMHbIQjDOGT0A9FFKJQKDMA6tPYRxJMisbNroCzxrKnsE491Fc3kPaCAiBzEGDBqQTb4ZGC8aHsI4/HegabpEL+CwaWhvVYzDjjNyEEg1BV0aBaCU2gvEAt/lQacmG0Sp2++eazSZEZF6wKtKqWcKs4xGo9GUdLQfajQazYOHiJQFtgJVlVKGYpZzz6PvgGpyRSm1D9gieVu3yRN4p5AkaTQaTbGg/VCj0WgeLESkP7ATGKs7nwWDvgOq0Wg0Go1Go9FoNJoiQd8B1Wg0Go1Go9FoNBpNkaA7oBqNRqPRaDQajUajKRJ0B1Sj0Wg0Go1Go9FoNEWC7oBqNBqNRqPRaDQajaZI0B1QjUaj0Wg0Go1Go9EUCf8Hz7seNpUTYZIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.plasma                                    # set the color map\n",
    "plt.subplot(131)                                        # location map of normal score transform of porosity\n",
    "GSLIB.locmap_st(df,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - All Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(132)                                        # location map of normal score transform of permeability\n",
    "GSLIB.locmap_st(df,'X','Y','NPerm',0,1000,0,1000,-3,3,'Nscore Permeability - All Facies','X (m)','Y (m)','Nscore Permeability',cmap)\n",
    "\n",
    "plt.subplot(133)\n",
    "facies = df['Facies'].values +0.01                      # normal score porosity / permeability scatter plot color coded by facies\n",
    "plt.scatter(df['NPor'],df['NPerm'],c = facies,edgecolor = 'black',cmap = plt.cm.inferno)\n",
    "#plt.plot([-3,3],[-3,3],color = 'black')\n",
    "plt.xlabel(r'Nscore Porosity')\n",
    "plt.ylabel(r'Nscore Permeability')\n",
    "plt.title('Nscore Permeability vs. Porosity')\n",
    "plt.xlim([-3,3])\n",
    "plt.ylim([-3,3])\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=0.8, wspace=0.5, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you see?  Here's my observations:\n",
    "\n",
    "* there is a high degree of spatial agreement between porosity and permeability, this is supported by the high correlation evident in the cross plot.\n",
    "* there are no discontinuities that could suggest that facies represent a distinct change, rather the porosity and permeability seem continuous and the assigned facies are a truncation of their continous behavoir, we doing 'ok' with no facies\n",
    "* suspect a 045 azimuth major direction of continuity (up - right)\n",
    "* there may be cycles in the 135 azimuth \n",
    "* there will not likely be a nugget effect, but there is an hint of some short scale discontinuity?\n",
    "\n",
    "**Do you agree?** If you have a different observations, drop me a line at mpyrcz@austin.utexas.edu and I'll add to this lesson with credit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Experimental Variograms\n",
    "\n",
    "We can use the location maps to help determine good variogram calculation parameters. For example:\n",
    "\n",
    "```p\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 100.0; lag_tol = 50.0; nlag = 7; bandh = 9999.9; azi = azi; atol = 22.5; isill = 1\n",
    "```\n",
    "* **tmin**, **tmax** are trimming limits - set to have no impact, no need to filter the data\n",
    "* **lag_dist**, **lag_tol** are the lag distance, lag tolerance - set based on the common data spacing (100m) and tolerance as 100% of lag distance for additonal smoothing\n",
    "* **nlag** is number of lags - set to extend just past 50 of the data extent\n",
    "* **bandh** is the horizontal band width - set to have no effect\n",
    "* **azi** is the azimuth -  it has not effect since we set atol, the azimuth tolerance, to 90.0\n",
    "* **isill** is a boolean to standardize the distribution to a variance of 1 - it has no effect since the previous nscore transform sets the variance to 1.0\n",
    "\n",
    "#### Dashboard for Interactive Variogram Calculation\n",
    "\n",
    "Below we make a dashboard with the ipywidgets and matplotlib Python packages for calculating experimental variograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interactive calculation of the experimental variogram\n",
    "l = widgets.Text(value='                              Variogram Calculation Interactive Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "lag = widgets.FloatSlider(min = 20, max = 500, value = 100, step = 10, description = 'lag',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "lag.style.handle_color = 'gray'\n",
    "\n",
    "lag_tol = widgets.FloatSlider(min = 20, max = 500, value = 50, step = 10, description = 'lag tolerance',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "lag_tol.style.handle_color = 'gray'\n",
    "\n",
    "nlag = widgets.IntSlider(min = 1, max = 100, value = 10, step = 1, description = 'number of lags',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "nlag.style.handle_color = 'gray'\n",
    "\n",
    "azi = widgets.FloatSlider(min = 0, max = 360, value = 0, step = 5, description = 'azimuth',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "azi.style.handle_color = 'gray'\n",
    "\n",
    "azi_tol = widgets.FloatSlider(min = 10, max = 90, value = 20, step = 5, description = 'azimuth tolerance',orientation='vertical',layout=Layout(width='120px', height='200px'))\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "bandwidth = widgets.FloatSlider(min = 100, max = 2000, value = 2000, step = 100, description = 'bandwidth',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "\n",
    "ui1 = widgets.HBox([lag,lag_tol,nlag,azi,azi_tol,bandwidth],) # basic widget formatting    \n",
    "ui = widgets.VBox([l,ui1],)\n",
    "\n",
    "def f_make(lag,lag_tol,nlag,azi,azi_tol,bandwidth):     # function to take parameters, calculate variogram and plot\n",
    "#    text_trap = io.StringIO()\n",
    "#    sys.stdout = text_trap\n",
    "    tmin = -9999.9; tmax = 9999.9\n",
    "    lags, gammas, npps = geostats.gamv(df,\"X\",\"Y\",\"NPor\",tmin,tmax,lag,lag_tol,nlag,azi,azi_tol,bandwidth,isill=1.0)\n",
    "    \n",
    "    plt.subplot(111)                                    # plot experimental variogram\n",
    "    plt.scatter(lags,gammas,color = 'black',s = npps*0.1,label = 'Azimuth ' +str(azi))\n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    plt.title('Directional NSCORE Porosity Variogram - Azi ' + str(azi))\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1.8])\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.5, top=1.0, wspace=0.3, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make, {'lag':lag,'lag_tol':lag_tol,'nlag':nlag,'azi':azi,'azi_tol':azi_tol,'bandwidth':bandwidth})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Variogram Calculation Demostration\n",
    "\n",
    "* calculate omnidirectional and direction experimental variograms \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Problem\n",
    "\n",
    "Calculate interpretable experimental variograms for sparse, irregularly-space spatial data.\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance\n",
    "\n",
    "* **number of lags** - number of lags in the experimental variogram\n",
    "\n",
    "* **bandwidth** - maximum departure from the lag vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa2f9da614834489bd7aebf1ee242f05",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                              Variogram Calculation Interactive Demonstration, Mich…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9bd45300d75a49958945a0c7f0fe7767",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui, interactive_plot)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of vairogram calculation for spatial continuity analysis. Much more could be done, I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)  \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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